Expert Opinion: Why Biostatistics Qualifications Matter in Med-Tech Industry Clinical Trials.

Consider the consequences if a medical doctor, without a formal medical education or licensing, were to diagnose and treat patients. Such a doctor might misunderstand symptoms, choose the wrong treatments, or even harm patients due to lack of understanding and experience. Similarly, an unqualified biostatistician might incorrectly analyse data, misinterpret statistical significance, or fail to recognise biases and patterns essential for accurate conclusions. These errors, when compounded across studies and publications, create a domino effect, misleading the medical community and affecting clinical guidelines that doctors worldwide follow.

When biostatistics work is flawed due to lack of proper training, the evidence that supports clinical decision-making is compromised. The gravity of these potential errors is amplified because biostatistics underpins clinical trial outcomes, which are often used to secure regulatory approval and define the standards for how to treat diseases. If flawed analysis leads to approving ineffective or harmful treatments, patients could suffer adverse effects from what they believe are safe therapies. In this sense, an unqualified biostatistician is even more dangerous than an unlicensed doctor, as their errors can influence the treatment decisions of countless doctors, each one putting their patients at risk based on incorrect or incomplete data.

Without proper qualifications, a biostatistician’s work can lead to harmful outcomes. This is because the analysis they perform underpins the scientific evidence that doctors rely on to make clinical decisions and guide patient care.


Why a Coursework Masters of Biostatistics an indispensable foundation

High-quality biostatistics programs offer advanced, in-depth training that goes far beyond basic statistical application. One of the core skills instilled is the ability to identify gaps in knowledge and continually adapt to the specific demands of each unique clinical trial. A competent biostatistician isn’t just someone who knows how to apply a set of methods; they are a problem-solver equipped to navigate complex, evolving situations, often needing to research, adapt, or even develop new techniques as each clinical context requires.

Unlike a research-based master’s thesis, which typically hones expertise in a narrow area, a coursework master’s in biostatistics emphasises a broad, structured understanding of the field, preparing individuals to apply statistical techniques accurately in a clinical context. Rigorous training in biostatistics is essential because the stakes are high, and the work of a biostatistician directly influences the treatment approaches trusted by healthcare providers around the world.

A hallmark of a quality biostatistics program is it’s focus on cultivating a mindset of critical evaluation and adaptability. Rather than simply learning a fixed set of methods, students are taught to understand the foundational principles of statistics and how to apply them thoughtfully across different clinical scenarios. This training includes learning how to question assumptions, test the validity of models, and assess the appropriateness of methods in light of each study’s design and data characteristics. It also involves learning how to identify situations where the standard, previously used methods may not suffice—an ability that can only come from a deep understanding of the mathematical principles underpinning statistical techniques.

The mathematical underpinnings of statistical tests can be subtle and intricate. Without specialised training, there’s a high risk that these mathematical nuances will be overlooked or mishandled. For example, failure to correctly adjust for confounding variables can make it appear as though a treatment effect exists when it doesn’t, leading to erroneous conclusions that could harm patients if implemented in clinical practice.

A well-prepared biostatistician is not only familiar with a wide range of statistical tools but also understands when each tool is appropriate, and more importantly, when it may be insufficient. Clinical trials often present unique challenges, such as complex interactions between variables, confounding factors, and datasets that may not conform neatly to traditional statistical models. In these cases, biostatisticians trained to think critically and independently can recognise that the standard approaches may fall short and are capable of researching novel methods, exploring the latest advancements, and adapting techniques to better fit the data at hand. This ability to assess, research, and innovate rather than rigidly apply textbook methods is what makes a biostatistician invaluable to clinical research.

Advanced biostatistics programs emphasise this flexibility, often incorporating coursework in emerging statistical methods, machine learning, and adaptive designs that are becoming increasingly relevant in modern clinical trials. These programs also provide hands-on training with real-world data, equipping students to handle the messy, imperfect datasets typical in clinical research. Graduates from rigorous programs gain the skills needed to work with a high degree of precision, recognising the limitations of each approach and adapting their methods to provide the most reliable analysis possible.

This commitment to continuous learning and adaptability is essential, particularly in a field as fast-evolving as clinical biostatistics. New statistical models, computational methods, and technologies are constantly emerging, offering powerful new ways to analyse data and uncover insights that would be missed with conventional methods. Biostatisticians trained to think critically and assess what they do not yet know are equipped to stay at the forefront of these advancements, ensuring that clinical trial data is analysed with the most effective and current techniques.

Individuals without this specialised training or with training from adjacent fields may lack this advanced skill set. While they may be familiar with statistical software and certain techniques, they often lack the deeper statistical grounding that allows them to identify gaps in their own knowledge, research novel techniques, and apply methods flexibly. They may rely more heavily on familiar, pre-existing methods, even when these approaches are suboptimal for the specific demands of a new clinical trial.

In clinical research, it’s critical to distinguish between fields that may seem related to biostatistics but lack the specialised training needed for rigorous clinical trial analysis. Adjacent disciplines such as biomedical engineering or bioinformatics, while valuable in their own right, do not provide the depth and specificity of statistical training required for high-stakes clinical biostatistics. Clinical trials demand a comprehensive understanding of advanced statistical methods, hypothesis testing, probability theory, and the practical challenges inherent in real-world clinical data. Without this foundation, there is a high risk that even a highly skilled professional in an adjacent field may misinterpret trial data or apply suboptimal models, potentially jeopardising trial results.

While adjacent fields like biomedical engineering and bioinformatics serve as valuable components to clinical research teams, they do not replace a biostatistician in terms of the depth of statistical expertise required to conduct clinical trials safely and effectively. Additionally, even within biostatistics itself, the rigour and quality of training can vary widely between institutions. A high-quality biostatistics qualification, grounded in coursework and practical experience, is essential to ensure that biostatisticians are fully prepared to meet the demands of clinical trial analysis, providing reliable evidence that healthcare providers can depend on to guide safe, effective patient care.


Core statistical concepts: Beyond Basic Stats


When we think about clinical trials, we often picture doctors, patients, and maybe lab scientists—but behind every trial is a biostatistician. They’re responsible for interpreting the data in a way that uncovers whether a treatment truly works, and just as importantly, whether it’s safe. On the surface, this might sound like standard statistics, but the reality is far more complex. Clinical trials involve intricate designs, variable data, and outcomes that hinge on precisely the right analytical approach. Here’s why a biostatistician needs a Master’s degree in biostatistics to navigate this terrain.


The Power Calculation: Not Just Plugging in Numbers

One of the most fundamental tasks in clinical trials is calculating statistical power—essentially, determining the sample size required to detect a treatment effect if it exists. While it might sound as simple as choosing a sample size, calculating power is actually a multi-layered process, filled with nuances that require advanced training.

A biostatistician needs to understand how effect size, variability, sample size, and study design all interact. For instance, they can’t simply use a pre-set formula; they must examine assumptions about the patient population, factor in dropout rates, and sometimes even simulate different scenarios to see how robust their sample size calculation is. If the sample size is too small, the study could miss a true treatment effect, leading to the incorrect conclusion that a treatment is ineffective. Too large, and it wastes resources and could expose patients to unnecessary risk.

An advanced biostatistics program should explore how to conduct sensitivity analyses, interpret simulation results, and understand the trade-offs in different power calculation approaches. These skills can be impractical to cultivate on the job without a solid foundation.


Hypothesis Testing: Far More Than Just a P-value

Hypothesis testing often gets reduced to p-values, but in clinical trials, p-values are just the tip of the iceberg. Deciding how to structure a hypothesis test is a skill that requires an in-depth understanding of the trial design, data type, and statistical limitations. P-values themselves are affected by factors like sample size and effect size, and they depend on correct assumptions about the data. If these assumptions are even slightly off, the results could be misleading. Additionally, a significant p value is not necessarily clinically meaningful – an effect size must be carefully considered.

Suppose a trial includes multiple subgroups, such as different age ranges, where treatment response might vary. A biostatistician needs to decide whether to test each group separately or combine them, taking into account the risk of inflating the false positive rate. They may have to employ adjustments like the Bonferroni correction or false discovery rate, each with its own implications for the results’ reliability. Knowing when and how to apply these adjustments requires expertise in statistical trade-offs—a skill set that goes beyond basic training.


Bayesian Modelling: The Complexity of Integrating Prior Information

In clinical trials, Bayesian modelling offers the flexibility to incorporate prior information, which can be crucial when there’s existing data on similar treatments. But building a Bayesian model is not as simple as adding a prior and letting the data “speak.” Bayesian analysis is an iterative, highly contextual process that involves understanding the nuances of prior selection, data updates, and model convergence.

For example, in a trial with limited data, the biostatistician might consider a prior based on past studies. But they need to ensure that the prior doesn’t overpower the current data, especially if the populations differ in meaningful ways. They’ll also have to assess how sensitive the model is to the chosen prior—small changes can have a large impact on the results. Once the model is built, they will test it with simulations, iteratively refine their approach, and apply computational techniques like Markov Chain Monte Carlo methods to ensure accurate estimates.

Core skills include how to choose and validate priors, handle computational challenges, and interpret Bayesian results in a way that is both statistically valid and clinically meaningful. Without this background, Bayesian methods could be misapplied, leading to conclusions that are overly dependent on prior data, potentially skewing the trial’s findings.


Handling Confounding Variables: Getting to the True Treatment Effect

Confounding variables are one of the most significant challenges in clinical trials. These are external factors that could influence both the treatment and the outcome, creating a false impression of effect. Managing confounding variables isn’t as simple as throwing all variables into a model. It involves selecting the right approach—whether that’s stratification, regression adjustment, or propensity score matching—to isolate the treatment’s actual impact.

Imagine a trial assessing the effect of a heart medication where younger patients tend to recover faster. If age isn’t properly accounted for, the results might suggest that the treatment is effective, simply because younger patients are overrepresented in the treatment group. Handling such confounding factors involves understanding the dependencies between variables, testing assumptions, and assessing the adequacy of different adjustment techniques.

Biostatistics programs address these complexities, teaching biostatisticians how to identify and handle confounders, use advanced models like inverse probability weighting, and validate their adjustments with sensitivity analyses. This is not something that can be mastered without a solid foundation in statistics and it’s application to medicine.

A practical example:


Consider a clinical trial evaluating an innovative cardiac monitoring device intended to reduce adverse cardiovascular events in a diverse patient population, with participants spanning a wide range of ages, co-morbidities, and cardiovascular risk profiles. The complexity of this study lies not only in the heterogeneity of the patient population but also in the need to accurately capture the device’s effectiveness over extended time periods and in varied real-world contexts. Here, standard statistical methods may fail to capture the full picture; without careful investigation and adaptation, these methods could miss critical variations in device effectiveness across different patient subgroups. Missteps in analysis could lead to misguided conclusions, resulting in the misapplication of the device or failure to recognise its specific benefits for certain populations.

An unqualified biostatistician, seeing only the broad structure of the trial, might select standard statistical approaches such as repeated measures analysis or proportional hazards models, assuming that the device’s impact can be summarised uniformly across patients and time. These methods, while effective in certain contexts, may oversimplify the true complexity of the data. For instance, these approaches may overlook significant patient-specific variations, assuming all patients respond similarly over time, and fail to address potential dependencies across repeated measurements. In doing so, they risk obscuring insights into how the device performs across age groups, co-morbidity profiles, or geographic regions.

A competent biostatistician, however, would recognise that such a complex, dynamic scenario demands a more tailored and investigative approach. They would start by reviewing trial specifics—population diversity, data structure, and endpoints—and identifying the particular challenges these present. This initial assessment might lead them to consider a range of advanced modelling techniques, from hierarchical models and frailty models to time-varying covariate models, evaluating each option to find the best fit for the study’s unique demands.

For instance, a hierarchical model could capture variability at multiple levels—such as individual patients, treatment centres, or geographic clusters—allowing the biostatistician to account for factors that might cluster within sites or subgroups. If, for example, patients from one geographic area tend to experience more adverse events, a hierarchical model would help isolate these effects, ensuring they don’t skew the treatment outcomes. A frailty model, on the other hand, might be more appropriate if there are unobserved variables influencing patient outcomes, such as genetic predispositions or lifestyle factors that impact how individuals respond to the device. Each model offers benefits but comes with specific assumptions and limitations, requiring the biostatistician to weigh these factors carefully.

The biostatistician would then move beyond selecting a method, entering a phase of critical evaluation and testing. They perform model diagnostics to check assumptions, such as independence and proportional hazards, assessing how well each model fits the trial data. If they find that patient characteristics change over time, influencing treatment response, they may pivot toward a time-varying covariate model. Such a model could capture how the effectiveness of the device changes with patient health fluctuations, an essential insight in trials where health status is dynamic. Rather than assuming proportional effects across time, this approach would allow the analysis to reflect real-world shifts in patient health and co-morbidity, enhancing the relevance of the results for long-term patient care.

In addition, the biostatistician may implement advanced stratification techniques or subgroup analyses, aiming to parse out the effects of specific co-morbidities like diabetes or chronic kidney disease. These approaches are not simply a matter of segmenting data; they require careful control of confounding variables and an understanding of how stratification affects power and interpretation. The biostatistician could explore techniques such as propensity score weighting or covariate balancing to create comparable subgroups, helping to isolate the device’s effect on each subgroup with minimal bias. This ensures that the treatment effect estimation is not conflated with unrelated patient characteristics, like age or pre-existing health conditions, which could distort the true efficacy of the device.

Because of the trial’s longitudinal design, the biostatistician would also need to research and carefully apply methods that accommodate time-dependent covariates. They might examine the appropriateness of flexible parametric survival models over the traditional Cox model, especially if patient health or response to treatment fluctuates significantly over time. By reviewing the latest literature and comparing models through simulation studies, the biostatistician can determine which methods best capture the time-varying nature of the data without introducing artefacts or biases. For instance, a flexible model might reveal periods during which the device is particularly effective, or it could show diminishing efficacy as patients’ health profiles evolve, offering critical insights into when and for whom the device provides the most benefit.

In this rigorous process, the biostatistician doesn’t simply apply methods—they conduct an iterative investigation, refining their approach with each step. Sensitivity analyses, for example, might be run to determine how robust findings are to different modelling choices or to evaluate the impact of unmeasured confounders. Through this iterative process, they test assumptions, explore the validity of each approach, and adjust techniques to ensure that their final analysis captures the device’s effectiveness in a nuanced, clinically relevant way. This stands in contrast to a one-size-fits-all analysis, where insights into key variations across patient subgroups may be lost.

Ultimately, the advanced approach adopted by a qualified biostatistician goes beyond statistical rigour—it provides a comprehensive, meaningful picture of the device’s real-world effectiveness. By thoroughly investigating and validating each method, the biostatistician ensures that their analysis accurately reflects how the device performs across diverse patient populations. This depth of analysis provides doctors with reliable, specific insights into which patients are most likely to benefit, supporting safer, more personalised treatment decisions in real-world clinical settings.

Biometrics & Clinical Trials Success:

Why Outsourcing a Biostatistics Team is Pivotal to the Success of your Clinical Trial

Clinical trials are among the most critical phases in bringing a medical device or pharmaceutical product to market, and ensuring the accuracy and integrity of the data generated is essential for success. While some companies may feel confident relying on their internal teams, especially if they have expertise in AI or data science, managing the full scope of biometrics in clinical trials often requires far more specialised skills. Building a dedicated in-house team may seem like a natural next step, but it can involve significant time, cost, and resource investment that can sometimes be underestimated.

Outsourcing biometrics services offers a streamlined, cost-effective alternative, providing access to a team of specialists in statistical programming, quality control, and regulatory compliance. Much like outsourcing marketing or legal services, entrusting biometrics to an external team allows businesses to focus on their core strengths while ensuring the highest standards of data accuracy and regulatory alignment. In this article, we explore why outsourcing biometrics is a smarter approach for clinical trials, offering the expertise, flexibility, and scalability needed to succeed.

1. Expertise Across Multiple Disciplines

Clinical trials require a blend of specialised skills, from statistical programming and data management to quality control and regulatory compliance. Managing these diverse requirements internally can stretch resources and may lead to oversights. When outsourcing to a biometrics team, companies can access a broad range of expertise across all these critical areas, ensuring that every aspect of the trial is handled by specialists in their respective fields.

Instead of spreading resources thin across a small internal team, outsourcing offers a more efficient approach where every key area is covered by experts, ultimately reducing the risk of errors and enhancing the quality of the trial data.


2. Avoid Bottlenecks and Delays

Managing the data needs of a clinical trial requires careful coordination, and internal teams can sometimes face bottlenecks due to workload or resource limitations. Unexpected delays, such as staff absences or project overload, can slow progress and increase the risk of missed deadlines.

Outsourcing provides built-in flexibility, where a larger, more experienced team can step in when needed, ensuring work continues without interruption. This kind of seamless handover keeps the trial on track and avoids the costly delays that might arise from trying to juggle too many responsibilities in-house.


3. Improved Data Quality Through Redundancy

One of the advantages of outsourcing biometrics is the added level of redundancy it offers. In-house teams, particularly small ones, may not have the capacity for thorough internal quality checks, potentially allowing errors to slip through.

Outsourced teams typically have multiple layers of review built into their processes. This ensures that data undergoes several levels of scrutiny, significantly reducing the risk of unnoticed mistakes and increasing the overall reliability of the analysis.


4. Flexibility and Scalability

The nature of clinical trials often shifts, with new sites, additional data points, or evolving regulatory requirements. This creates a demand for scalability in managing the trial’s data. Internal teams can struggle to keep up as the project grows, sometimes leading to bottlenecks or rushed work that compromises quality.

Outsourcing biometrics allows companies to adapt to the changing scope of a trial easily. A specialised team can quickly scale its operations to handle additional workload without compromising the timeline or quality of the analysis.


5. Ensuring Regulatory Compliance

Meeting regulatory requirements is a critical aspect of any clinical trial. From meticulous data documentation to adherence to best practices, there are stringent standards that must be followed to gain approval from bodies like the FDA or EMA.

Outsourcing to an experienced biometrics team ensures that these standards are met consistently. Having worked across multiple trials, outsourced teams are well-versed in the latest regulations and can ensure that all aspects of the trial meet the necessary compliance requirements. This reduces the risk of costly rejections or trial delays caused by non-compliance.


6. Enhanced Data Security and Infrastructure

Handling sensitive clinical trial data requires secure systems and advanced infrastructure, which can be costly for companies to manage internally. Maintaining this infrastructure, along with the necessary cybersecurity measures, can quickly escalate expenses, especially for smaller in-house teams.

By outsourcing biometrics, companies gain access to teams with pre-existing secure infrastructure designed specifically for clinical data. This not only reduces costs but also mitigates the risk of data breaches, ensuring compliance with privacy regulations like GDPR.


7. Hidden Challenges of Building an In-House Team

While building an in-house biometrics team might seem appealing, it comes with it’s hidden challenges and costs that are easily overlooked. Recruitment, training, administrative load and retention all contribute to a growing budget, along with HR costs and the ongoing need to invest in tools and advanced infrastructure to keep the team effective.

Outsourcing offers a clear financial benefit here. Companies can bypass many resource draining activities and gain immediate access to a team of experts, without having to worry about ongoing staff management or the investment in specialised tools.


8. Unbiased Expertise

Internal teams may face pressure to align with existing company practices or preferences, which can sometimes lead to biased decisions when it comes to methodology or quality control. Outsourced teams are entirely independent and focused solely on delivering objective, high-quality results. This ensures that the best statistical methods are applied, without the potential for internal pressures to sway critical decisions.


The Case for Outsourcing Biometrics

Clinical trials are complex and require a range of specialised skills to ensure their success. While building an in-house team might seem like an intuitive solution, it often introduces unnecessary risks, hidden costs, and logistical challenges. Outsourcing biometrics to a specialised team offers a streamlined, scalable solution that ensures trial data is handled with precision and integrity, while maintaining regulatory compliance.

By leveraging the expertise of an external biometrics team, companies can focus on their core strengths—whether it’s developing a breakthrough medical device or innovating in their field—while leaving the complexities of biometrics to the experts.


If you’re preparing for your next clinical trial and want to ensure
reliable, accurate, and compliant results, contact Anatomise Biostats
today. Our expert biometrics team is ready to support your project
and deliver the results you need to bring your medical device to
market with confidence.


Stata: Statistical Software for Regulatory Compliance in Clinical Trials

Stata is widely used in various research domains such as economics, biosciences, health and social sciences, including clinical trials. It has been utilised for decades in studies published in reputable scientific journals. While SAS has a longer history of being explicitly referenced by regulatory agencies such as the FDA, Stata can still meet regulatory compliance requirements in clinical trials. StataCorp actively engages with researchers, regulatory agencies, and industry professionals to address compliance needs and provide technical support, thereby maintaining a strong commitment to producing high-quality software and staying up to date with industry standards.

Stata’s commitment to accuracy, comprehensive documentation, integrated versioning, and rigorous certification processes provides researchers with a reliable and compliant statistical software for regulatory submissions. Stata’s worldwide reputation, excellent technical support, seamless verification of data integrity, and ease of obtaining updates further contribute to its suitability for clinical trials and regulatory compliance.

To facilitate regulatory compliance in clinical trials, Stata offers features such as data documentation and audit trails, allowing researchers to document and track data manipulation steps for reproducibility and transparency. Stata’s built-in “do-files” and “log-files” can capture commands and results, aiding in the audit trail process. Stata provides the flexibility to generate analysis outputs and tables in formats commonly required for regulatory reporting (e.g., PDF, Excel, or CSV). It also enables the automation of reproducible, fully-formatted publication standard reports. Strong TLF and CRF programming used to be the domain of SAS which explains their early industry dominance. SAS was developed in 1966 using funding from the National Institute of Health. In recent years, however, Stata has arguably surpassed what is achievable in SAS with the same efficiency, particularly in the context of clinical trials.

Stata has extensive documentation of adaptive clinical trial design. Adaptive group sequential designs can be achieved using the GDS functionality. The default graphs and tables produced using GDS analysis really do leave SAS in the dust being more visually appealing and easily interpretable. They are also more highly customisable than what can be produced in SAS. Furthermore the Stata syntax used to produce them is minimal compared to corresponding SAS commands, while still retaining full reproducibility.

Stata’s comprehensive causal inference suite enables experimental-style causal effects to be derived from observational data. This can be helpful in planning clinical trials based on observed patient data that is already available, with the process being fully documentable.

Advanced data science methods are being increasingly used in clinical trial design and planning as well as for follow-up exploratory analysis of clinical trial data. Stata has both supervised and unsupervised machine learning capability in its own right for decades. Stata can also integrate with other tools and programming languages, such as Python for PyStata and PyTrials, if additional functionalities or specific formats are needed. This can be instrumental for advanced machine learning and other data science methods goes beyond native features and user-made packages in terms of customisability. Furthermore, using Python within the Stata interface allows for compliant documentation of all analyses. Python integration is also available in SAS via numerous packages and is able to eliminate some of the limitations of native SAS, particularly when it comes to graphical outputs.

Stata for FDA regulatory compliance

While the FDA does not mandate the use of any specific statistical software, they emphasise the need for reliable software with appropriate documentation of testing procedures. Stata satisfies the requirements of the FDA and is recognized as one of the most respected and validated statistical tools for analysing clinical trial data across all phases, from pre-clinical to phase IV trials. With Stata’s extensive suite of statistical methods, data management capabilities, and graphics tools, researchers can rely on accurate and reproducible results at every step of the analysis process.

When it comes to FDA guidelines on statistical software, Stata offers features that assist in compliance. Stata provides an intuitive Installation Qualification tool that generates a report suitable for submission to regulatory agencies like the FDA. This report verifies that Stata has been installed properly, ensuring that the software meets the necessary standards.

Stata offers several key advantages when it comes to FDA regulatory compliance for clinical trials. Stata takes reproducibility seriously and is the only statistical package with integrated versioning. This means that if you wrote a script to perform an analysis in 1985, that same script will still run and produce the same results today. Stata ensures the integrity and consistency of results over time, providing reassurance when submitting applications that rely on data and results from clinical trials.

Stata also offers comprehensive manuals that detail the syntax, use, formulas, references, and examples for all commands in the software. These manuals provide researchers with extensive documentation, aiding in the verification and validity of data and analyses required by the FDA and other regulatory agencies.

To further ensure computational validity, Stata undergoes extensive software certification testing. Millions of lines of certification code are run on all supported platforms (Windows, Mac, Linux) with each release and update. Any discrepancies or changes in results, output, behaviour, or performance are thoroughly reviewed by statisticians and software engineers before making the updated software available to users. Stata’s accuracy is also verified through the National Institute of Standards (NIST) StRD numerical accuracy tests and the George Marsaglia Diehard random-number generator tests.

Data management in Stata

Stata’s Datasignature Suite and other similar features offer powerful tools for data validation, quality control, and documentation. These features enable users to thoroughly examine and understand their datasets, ensuring data integrity and facilitating transparent research practices. Let’s explore some of these capabilities:

  1. Datasignature Suite:

The Datasignature Suite is a collection of commands in Stata that assists in data validation and documentation. It includes commands such as `datasignature` and `dataex`, which provide summaries and visualizations of the dataset’s structure, variable types, and missing values. These commands help identify inconsistencies, outliers, and potential errors in the data, allowing users to take appropriate corrective measures.

2. Variable labelling:

 Stata allows users to assign meaningful labels to variables, enhancing data documentation and interpretation. With the `label variable` command, users can provide descriptive labels to variables, making it easier to understand their purpose and content. This feature improves collaboration among researchers and ensures that the dataset remains comprehensible even when shared with others.

3. Value labels:

 In addition to variable labels, Stata supports value labels. Researchers can assign descriptive labels to specific values within a variable, transforming cryptic codes into meaningful categories. Value labels enhance data interpretation and eliminate the need for constant reference to codebooks or data dictionaries.

4. Data documentation:

Stata encourages comprehensive data documentation through features like variable and dataset-level documentation. Users can attach detailed notes and explanations to variables, datasets, or even individual observations, providing context and aiding in data exploration and analysis. Proper documentation ensures transparency, reproducibility, and facilitates data sharing within research teams or with other stakeholders.

5. Data transformation:

Stata provides a wide range of data transformation capabilities, enabling users to manipulate variables, create new variables, and reshape datasets. These transformations facilitate data cleaning, preparation, and restructuring, ensuring data compatibility with statistical analyses and modelling procedures.

6. Data merging and appending:

Stata allows users to combine multiple datasets through merging and appending operations. By matching observations based on common identifiers, researchers can consolidate data from different sources or time periods, facilitating longitudinal or cross-sectional analyses. This feature is particularly useful when dealing with complex study designs or when merging administrative or survey datasets.

7. Data export and import:

Stata offers seamless integration with various file formats, allowing users to import data from external sources or export datasets for further analysis or sharing. Supported formats include Excel, CSV, SPSS, SAS, and more. This versatility enhances data interoperability and enables collaboration with researchers using different software.

These features collectively contribute to data management best practices, ensuring data quality, reproducibility, and documentation. By leveraging the Datasignature Suite and other data management capabilities in Stata, researchers can confidently analyse their data and produce reliable results while maintaining transparency and facilitating collaboration within the scientific community.

Stata and maintaining CDISC standards. How does it compare to SAS?

Stata and SAS are both statistical software packages commonly used in the fields of data analysis, including in the pharmaceutical and clinical research industries. While they share some similarities, there are notable differences between the two when it comes to working with CDISC standards:

  1. CDISC Support:

SAS has extensive built-in support for CDISC standards. SAS provides specific modules and tools, such as SAS Clinical Standards Toolkit, which offer comprehensive functionalities for CDASH, SDTM, and ADaM. These modules provide pre-defined templates, libraries, and validation rules, making it easier to implement CDISC standards directly within the SAS environment. Stata, on the other hand, does not have native, dedicated modules specifically designed for CDISC standards. However, Stata’s flexibility allows users to implement CDISC guidelines through custom programming and data manipulation.

2. Data Transformation:

SAS has robust built-in capabilities for transforming data into SDTM and ADaM formats. SAS provides specific procedures and functions tailored for SDTM and ADaM mappings, making it relatively straightforward to convert datasets into CDISC-compliant formats. Stata, while lacking specific CDISC-oriented features, offers powerful data manipulation functions that allow users to reshape, merge, and transform datasets. Stata users may need to develop custom programming code to achieve CDISC transformations.

3. Industry Adoption:

SAS has been widely adopted in the pharmaceutical industry and is often the preferred choice for CDISC-compliant data management and analysis. Many pharmaceutical companies, regulatory agencies, and clinical research organizations have established workflows and processes built around SAS for CDISC standards. Stata, although less commonly associated with CDISC implementation, is still a popular choice for statistical analysis across various fields, including healthcare and social sciences. Stata has the potential to make adherence to CDISC standards a more affordable option for small companies and therefore an increased priority.

4. Learning Curve and Community Support:

SAS has a long been the default preference in the context of CDISC compliance and is what statistical programmers are used to, thus SAS is known for its comprehensive documentation and extensive user community. Resources including training materials, user forums, and user groups, which can facilitate learning and support for CDISC-related tasks. Stata also has an active user community and provides detailed documentation, but its community may be comparatively smaller in the context of CDISC-specific workflows. Stata has the advantage of reducing the amount of programming required to achieve CDISC compliance, for example in the creation of SDTM and ADaM data sets.

While SAS offers dedicated modules and tools specifically designed for CDISC standards, Stata provides flexibility and powerful data manipulation capabilities that can be leveraged to implement CDISC guidelines. The choice between SAS and Stata for CDISC-related work may depend on factors such as industry norms, organizational preferences, existing infrastructure, and individual familiarity with the software.

While SAS has historically been more explicitly associated with regulatory compliance in the clinical trial domain, Stata is fully equipped to fulfil regulatory requirements and has been utilised effectively in clinical research since. Researchers often choose the software they are most comfortable with and consider factors such as data analysis capabilities, familiarity, and support when deciding between SAS and Stata for their regulatory compliance needs.

It is important to note that compliance requirements can vary based on specific regulations and guidelines. Researchers are responsible for ensuring their analysis and reporting processes align with the appropriate regulatory standards and should consult relevant regulatory authorities when necessary.

The Devil’s Advocate: Stata for Clinical Study Design, Data Processing, & Statistical Analysis of Clinical Trials.

Stata is a powerful statistical analysis software that offers some advantages for clinical trial and medtech use cases compared to the more widely used SAS software. Stata provides an intuitive and user-friendly interface that facilitates efficient data management, data processing and statistical analysis. Its agile and concise syntax allows for reproducible and transparent analyses, enhancing the overall research process with more readily accessible insights.

Distinct from R, which incorporates S based coding, both Stata and SAS have used C based programming languages since 1985.  All three packages can parse full Python within their environment for advanced machine learning capabilities, in addition to those available natively. In Stata’s case this is achieved through the pystata python package. Despite a common C based language, there are tangible differences between Stata and SAS syntax. Stata generally needs less lines of code on average compared to SAS to perform the same function and thus tends to be more concise. Stata also offers more flexibility to how you code as well as more informative error statements which makes debugging a quick and easy process, even for beginners.

When it comes to simulations and more advanced modelling our experience had been that the Basic Edition of Stata (BE) is faster and uses less memory to perform the same task compared to Base SAS. Stata BE certainly has more inbuilt capabilities than you would ever need for the design and analysis of advanced clinical trials and sophisticated statistical modelling of all types. There is also the additional benefit of thousands of user-built packages, such as the popular WinBugs, that can be instantly installed as add-ons at no extra cost. Often these packages are designed to make existing Stata functions even more customisable for immense flexibility and programming efficiency.  Both Stata and SAS represent stability and reliability and have enjoyed widespread industry adoption. SAS has been more widely adopted by big pharma and Stata more-so with public health and economic modelling. 

It has been nearly a decade since the Biostatistics Collaboration of Australia (BCA) which determines Biostatistics education nationwide has transitioned from teaching SAS and R as part of their Masters of Biostatistics programs to teaching Stata and R. This transition initially was made in anticipation of an industry-wide shift from SAS to Stata. Whether their predictions were accurate or not, the case for Stata use in clinical trials remains strong.

Stata is almost certainly a superior option for bootstrapped life science start-ups and SMEs. Stata licencing fees are in the low hundreds of pounds with the ability to quickly purchase over the Stata website, while SAS licencing fees span the tens to hundreds of thousands and often involve a drawn-out process just to obtain a precise quote.

Working with a CRO that is willing to use Stata means that you can easily re-run any syntax provided from the study analysis to verify or adapt it later. Of course, open-source software such as R is also available, however Stata has the advantage of a reduced learning curve being both user-friendly and sufficiently sophisticated.

Stata for clinical trials

  1. Industry Adoption:

Stata has gained significant popularity and widespread adoption in the field of clinical research. It is commonly used by researchers, statisticians, and healthcare professionals for the statistical analysis of clinical data.

2. Regulatory Compliance and CDISC standardisation:

Stata provides features and capabilities that support regulatory compliance requirements in clinical trials. While it may not have the same explicit recognition from CDISC as SAS, Stata does lend itself well to CDISC compliance and offers tools for documentation, data tracking, and audit trails to ensure transparency and reproducibility in analyses.

3. Comprehensive Statistical Procedures:

A key advantage of Stata is its extensive suite of built-in statistical functions and commands specifically designed for clinical trial data analysis. Stata offers a wide range of methods for handling missing data, performing power calculations, and of course a wide range of methods for analysing clinical trial data; from survival analysis methods, generalized linear models, mixed-effects models, causal inference, and Bayesian simulation for adaptive designs. Preparatory tasks for clinical trials such as meta-analysis, sample size calculation and randomisation schedules are arguably easier to achieve in Stata than SAS. These built-in functionalities empower researchers to conduct various analyses within a single software environment.

4. Efficient Data Management:

Stata excels in delivering agile data management capabilities, enabling efficient data handling, cleaning, and manipulation. Its intuitive data manipulation commands allow researchers to perform complex transformations, merge datasets, handle missing data, and generate derived variables seamlessly.

Perhaps the greatest technical advantage of Stata over SAS in the context of clinical research is usability and greater freedom to keep open and refer to multiple data sets with multiple separate analyses at the same time. While SAS can keep many data sets in memory for a single project, Stata can keep many data sets in siloed memory for simultaneous use in different windows to enable viewing or working on many different projects at the same time. This approach can make workflow easier because no data step is required to identify which data set you are referring to, instead the appropriate sections of any data sets can be merged with the active project as needed and due to siloing, which works similarly to tabs in a browser, you do not get the log, data or output of one project mixed up with another. This is arguably an advantage for biostatisticians and researchers alike who typically do need to compare unrelated data sets or the statistical results from separate studies side-by-side.

5. Interactive and Reproducible Analysis:

Stata provides an interactive programming environment that allows users to perform data analysis in a step-by-step manner. The built-in “do-file” functionality facilitates reproducibility by capturing all commands and results, ensuring transparency and auditability of the analysis process. The results and log window for each data set prints out the respective syntax required item by item. This syntax can easily be pasted into the do-file or the command line to edit or repeat the command with ease. SAS on the other hand tends to separate the results from the syntax used to derive it.

6. Graphics and Visualization:

While not traditionally known for this, Stata actually offers a wide range of powerful and customizable graphical capabilities. Researchers can generate high-quality publication standard  plots and charts of any description needed to visualise clinical trial results Common examples include survival curves, forest plots, spaghetti and diagnostic plots. Stata also has built-in options to perform all necessary assumption and model checking for statical model development.

These visualisations facilitate the exploration and presentation of complex data patterns, as well as the presentation, and communication of findings. There are many user-created customisation add-ons for data visualisation that rival what is possible in R customisation.

The one area of Stata that users may find limiting is that it is only possible to display one graph at a time per active data set. This means that you do need to copy graphs as they are produced and save them into a document to compare multiple graphs side by side.

7. Active User Community and Support:

Like SAS, Stata has a vibrant user community comprising researchers, statisticians, and experts who actively contribute to discussions, share knowledge, and provide support. StataCorp, the company behind Stata, offers comprehensive documentation, online resources, and user forums, ensuring users have access to valuable support and assistance when needed. Often the resources available for Stata are more direct and more easily searchable than what is available for SAS when it comes to solving customisation quandaries. This is of course bolstered by the availability of myriad instant package add-ons.

Stata’s active and supportive user community is a notable advantage. Researchers can access extensive documentation, online forums, and user-contributed packages, which promote knowledge sharing and facilitate problem-solving. Additionally, Stata’s reputable technical support ensures prompt assistance for any software-related queries or challenges.

While SAS and Stata have their respective strengths, Stata’s increasing industry adoption, statistical capabilities, data management features, reproducibility, visualisation add-ons, and support community make it a compelling choice for clinical trial data analysis.

As it stands, SAS remains the most widely used software in big-pharma for clinical trial data analysis. Stata however offers distinct advantages in terms of user-friendliness, tailored statistical functionalities, advanced graphics, and a supportive user community. Consider adopting Stata to streamline your clinical trial analyses and unlock its vast potential for gaining insights from research outcomes. An in-depth overview of Stata 18 can be found here. A summary of it’s features for biostatisticians can be found here.

Further reading:

Using Stata for Handling CDISC Complient Data Sets and Outputs (lexjansen.com)

Sex Differences in Clinical Trial Recruiting

The following article investigates several systematic reviews into sex and gender representation in individual clinical trial patient populations. In these studies sex ratios are assessed and evaluated by various factors such as clinical trial phase, disease type under investigation and disease burden in the population. Sex differences in the reporting of safety and efficacy outcomes are also investigated. In many cases safety and efficacy outcomes are pooled, rather than reported individually for each sex, which can be problematic when findings are generalised to the wider population. In order to get the dosage right for different body compositions and avoid unforeseen outcomes in off label use or when a novel therapeutic first reaches the market, it is important to report sex differences in clinical trials. Due to the unique nuances of disease types and clinical trial phases it is important to realise that a 50-50 ratio of male to female is not always the ideal or even appropriate in every clinical study design. Having the right sex balance in your clinical trial population will improve the efficiency and cost-effectiveness of your study. Based upon the collective findings a set of principles are put forth to guide the researcher in determining the appropriate sex ratio for their clinical trial design.

Sex difference by clinical trial phase

  • variation in sex enrolment ratios for clinical trial phases
  • females less likely to participate in early phases, due to increased risk of adverse events
  • under-representation of women in phase III when looking at disease prevalence

It has been argued that female representation in clinical trials is lacking, despite recent efforts to mitigate the gap. US data from 2000-2020 suggests that trial phase has the greatest variation in enrolment when compared to other factors, with median female enrolment being 42.9%, 44.8%, 51.7%, and 51.1% for phases I, I/II to II, II/III to III, and IV4. This shows that median female enrolment gradually increases as trials progress, with the difference in female enrolment between the final phases II/III to III and IV being <1%. Additional US data on FDA approved drugs including trials from as early as 1993 report that female participation in clinical trials is 22%, 48%, and 49% for trial phases I, II, and III respectively2. While the numbers for participating sexes are almost equal in phases II and III, women make up only approximately one fifth of phase I trial populations in this dataset2. The difference in reported participation for phase I trials between the datasets could be due to an increase in female participation in more recent years. The aim of a phase I trial is to evaluate safety and dosage, so it comes as no surprise that women, especially those of childbearing age, are often excluded due to potential risks posed to foetal development.

In theory, women can be included to a greater extent as trial phases progress and the potential risk of severe adverse events decreases. By the time a trial reaches phase III, it should ideally reflect the real-world disease population as much as possible. European data for phase III trials from 2011-2015 report 41% of participants being female1, which is slightly lower than female enrolment in US based trials. 26% of FDA approved drugs have a >20% difference between the proportion of women in phase II & III clinical trials and the prevalence of women in the US with the disease2, and only one of these drugs shows an over-representation of women.

Reporting of safety and efficacy by sex difference

  • Both safety and efficacy results tend to differ by sex.
  • Reporting these differences is inconsistent and often absent
  • Higher rates of adverse events in women are possibly caused by less involvement or non stratification in dose finding and safety studies.
  • There is a need to enforce analysis and reporting of sex differences in safety and efficacy data

Sex differences in response to treatment regarding both efficacy and safety have been widely reported. Gender subgroup analyses regarding efficacy can reveal whether a drug is more or less effective in one sex than the other. Gender subgroup analyses for efficacy are available for 71% of FDA approved drugs, and of these 11% were found to be more efficacious in men and 7% in women2. Alternatively, only 2 of 22 European Medicines Agency approved drugs examined were found to have efficacy differences between the sexes1. Nonetheless, it is important to study the efficacy of a new drug on all potential population subgroups that may end up taking that drug.

The safety of a treatment also differs between the sexes, with women having a slightly higher percentage (p<0.001) of reported adverse events (AE) than men for both treatment and placebo groups in clinical trials1. Gender subgroup analyses regarding safety can offer insights into the potential risks that women are subjected to during treatment. Despite this, gender specific safety analyses are available for only 45% of FDA approved drugs, with 53% of these reporting more side effects in women2. On average, women are at a 34% increased risk of severe toxicity for each cancer treatment domain, with the greatest increased risk being for immunotherapy (66%). Moreover, the risk of AE is greater in women across all AE types, including patient-reported symptomatic (female 33.3%, male 27.9%), haematologic (female 45.2%, male 39.1%) and objective non-haematologic (female 30.9%, male 29.0%)3. These findings highlight the importance of gender specific safety analyses and the fact that more gender subgroup safety reporting is needed. More reporting will increase our understanding of sex-related AE and could potentially allow for sex-specific interventions in the future.

Sex differences by disease type and burden

  • Several disease categories have recently been associated with lower female enrolment
  • Men are under-represented as often as women when comparing enrolment to disease burden proportions
  • There is a need for trial participants to be recruited on a case-by-case basis, depending on the disease.

Sex differences by disease type

When broken down by disease type, the sex ratio of clinical trial participation shows a more nuanced picture. Several disease categories have recently been associated with lower female enrolment, compared to other factors including trial phase, funding, blinding, etc4. Women comprised the smallest proportions of participants in US-based trials between 2000-2020 for cardiology (41.4%), sex-non-specific nephrology and genitourinary (41.7%), and haematology (41.7%) clinical trials4. Despite women being

proportionately represented in European phase III clinical studies between 2011-2015 for depression, epilepsy, thrombosis, and diabetes, they were significantly under-represented for hepatitis C, HIV, schizophrenia, hypercholesterolaemia, and heart failure and were not found to be overrepresented in trials for any of the disease categories examined1. This shows that the gap in gender representation exists even in later clinical trial phases when surveying disease prevalence, albeit to a lesser extent. Examining disease burden shows that the gap is even bigger than anticipated and includes the under-representation of both sexes.

Sex Differences by Disease Burden

It is not until the burden of disease is considered that men are shown to be under-represented as often as women. Including burden of disease can depict proportionality relative to the variety of disease manifestations between men and women. It can be measured as disability-adjusted life years (DALYs), which represent the number of healthy years of life lost due to the disease. Despite the sexes each making up approximately half of clinical trial participants overall in US-based trials between 2000-2020, all disease categories showed an under-representation of either women or men relative to disease burden, except for infectious disease and dermatologic clinical trials4. Women were under-represented in 7 of 17 disease categories, with the greatest under-representation being in oncology trials, where the difference between the number of female trial participants and corresponding DALYs is 3.6%. Men were under-represented compared with their disease burden in 8 of 17 disease categories, with the greatest difference being 11.3% for musculoskeletal disease and trauma trials.4 Men were found to be under-represented to a similar extent to women, suggesting that the under-representation of either sex could be by coincidence. Alternatively, male under-representation could potentially be due to the assumption of female under-representation leading to overcorrection in the opposite direction. It should be noted that these findings would benefit from statistical validation, although they illustrate the need for clinical trial participants to be recruited on a case-by-case basis, depending on the disease.

Takeaways to improve your patient sample in clinical trial recruiting:

  1. Know the disease burden/DALYs of your demographics for that disease.
  2. Try to balance the ratio of disease burden to the appropriate demographics for your disease
  3. Aim to recruit patients based on these proportions
  4. Stratify clinical trial data by the relevant demographics in your analysis. For example: toxicity, efficacy, adverse events etc should always be analyses separately for male and female to come up wit the respective estimates.
  5. Efficacy /toxicity etc should always be reported separately for male and female. reporting difference by ethnicity is also important as many diseases differentially affect certain ethnicity and the corresponding therapeutics can show differing degrees of efficacy and adverse events.

The end goal of these is that medication can be more personalised and any treatment given is more likely to help and less likely to harm the individual patient.

Conclusions

There is room for improvement in the proportional representation of both sexes in clinical trials and knowing a disease demographic is vital to planning a representative trial. Assuming the under-representation is on the side of female rather than male may lead to incorrect conclusions and actions to redress the balance. Taking demographic differences in disease burden into account when recruiting trial participants is needed. Trial populations that more accurately depict the real-world populations will allow a therapeutic to be tailored to the patient.

Efficacy and safety findings highlight the need for clinical study data to be stratified by sex, so that respective estimates can be determined. This enables more accurate, sex/age appropriate dosing that will maximise treatment efficacy and patient safety, as well as minimise the chance of adverse events. This also reduces the risks associated with later off label use of drugs and may avoid modern day tragedies resembling the thalidomide tragedy. Moreover, efficacy and adverse events should always be reported separately for men and women, as the evidence shows their distinct differences in response to therapeutics.

See our full report on diversity in patient recruiting for clinical trials.

References:

1. Dekker M, de Vries S, Versantvoort C, Drost-van Velze E, Bhatt M, van Meer P et al. Sex Proportionality in Pre-clinical and Clinical Trials: An Evaluation of 22 Marketing Authorization Application Dossiers Submitted to the European Medicines Agency. Frontiers in Medicine. 2021;8.

2. Labots G, Jones A, de Visser S, Rissmann R, Burggraaf J. Gender differences in clinical registration trials: is there a real problem?. British Journal of Clinical Pharmacology. 2018;84(4):700-707.

3. Unger J, Vaidya R, Albain K, LeBlanc M, Minasian L, Gotay C et al. Sex Differences in Risk of Severe Adverse Events in Patients Receiving Immunotherapy, Targeted Therapy, or Chemotherapy in Cancer Clinical Trials. Journal of Clinical Oncology. 2022;40(13):1474-1486.

4. Steinberg J, Turner B, Weeks B, Magnani C, Wong B, Rodriguez F et al. Analysis of Female Enrollment and Participant Sex by Burden of Disease in US Clinical Trials Between 2000 and 2020. JAMA Network Open. 2021;4(6):e2113749.

Estimating the Costs Associated with Novel Pharmaceutical development: Methods and Limitations.

Data sources for cost analysis of drug development R&D and clinical trials

Cost estimates for pre-clinical and clinical development across the pharmaceutical industry differ based on several factors. One of these is the source of data used by each costing study to inform these estimates. Several studies use private data, which can include confidential surveys filled out by pharmaceutical firms/clinical trial units and random samples from private databases3,9,10,14,15,16. Other studies have based their cost estimates upon publicly available data, such as data from the FDA/national drug regulatory agencies, published peer-reviewed studies, and other online public databases1,2,12,13,17.

Some have questioned the validity of using private surveys from large multinational pharmaceutical companies to inform cost estimates, saying that survey data may be artificially inflated by pharmaceutical companies to justify high therapeutic prices 18,19,20. Another concern is that per trial spending by larger pharmaceutical companies and multinational firms would far exceed the spending of start-ups and smaller firms, meaning cost estimates made based on data from these larger companies would not be representative of smaller firms.

Failure rate of R&D and clinical trial pipelines

Many estimates include the cost of failures, which is especially the case for cost estimates “per approved drug”. As many compounds enter the clinical trial pipeline, the cost to develop one approved drug/compound includes cost of failures by considering the clinical trial success rate and cost of failed compounds. For example, if 100 compounds enter phase I trials, and 2 compounds are approved, the clinical cost per approved drug would include the amount spent on 50 compounds.

The rate of success used can massively impact cost estimates, where a low success rate or high failure rate will lead to much higher costs per approved drug. The overall probability of clinical success may vary by year and has been estimated at a range of values including 7.9%21, 11.83%10, and 13.8%22. There are concerns that some studies suggesting lower success rates have relied on small samples from industry curated databases and are thereby vulnerable to selection bias12,22.

Success rates per phase transition also affects overall costs. When more ultimately unsuccessful compounds enter late clinical trial stages, the higher the costs are per approved compound. In addition, success rates are also dependent on therapeutic area and patient stratification by biomarkers, among other factors. For example, one study estimated the lowest success rate at 1.6% for oncological trials without biomarker use compared with a peak success rate of 85.7% for cardiovascular trials utilising biomarkers22. While aggregate success rates can be used to estimate costs, using specific success rates will be more accurate to estimate the cost of a specific upcoming trial, which could help with budgeting and funding decisions.

Out-of-pocket costs vs capitalised costs & interest rates

Cost estimates also differ due to reporting of out-of-pocket and capitalised costs. An out-of-pocket cost refers to the amount of money spent or expensed on the R&D of a therapeutic. This can include all aspects of setting up therapeutic development, from initial funding in drug discovery/device design, to staff and site costs during clinical trials, and regulatory approval expenses.

The capitalised cost of a new therapeutic refers to the addition of out-of-pocket costs to a yearly interest rate applied to the financial investments funding the development of a new drug. This interest rate, referred to as the discount rate, is determined by (and is typically greater than) the cost of capital for the relevant industry.

Discount rates for the pharmaceutical industry vary between sources and can dramatically alter estimates for capitalised cost, where a higher discount rate will increase capitalised cost. Most studies place the private cost of capital for the pharmaceutical industry to be 8% or higher23,24, while the cost of capital for government is lower at around 3% to 7% for developed countries23,25. Other sources have suggested rates from as high as 13% to as low as zero13,23,26, where the zero cost of capital has been justified by the idea that pharmaceutical firms have no choice but to invest in R&D. However, the mathematical model used in many estimations for the cost of industry capital, the CAPM model, tends to give more conservative estimates23. This would mean the 10.5% discount rate widely used in capitalised cost estimates may in fact result in underestimation.

While there is not a consensus on what discount rate to use, capitalised costs do represent the risks undertaken by research firms and investors. A good approach may be to present both out-of-pocket and capitalised estimated costs, in addition to rates used, justification for the rate used, and the estimates using alternative rates in a sensitivity analysis26.

Costs variation over time

The increase in therapeutic development costs

Generally, there has been a significant increase in the estimated costs to develop a new therapeutic over time26. One study reported an exponential increase of capitalised costs from the 1970s to the mid-2010s, where the total capitalised costs rose annually 8.5% above general inflation from 1990 to 201310. Recent data has suggested that average development costs reached a peak in 2019 and had decreased the following two years9. This recent decrease in costs was associated with slightly reduced cycle times and an increased proportion of infectious disease research, likely in response to the rapid response needed for COVID-19.

Recent cost estimates

Costs can range with more than 100-fold differences for phase III/pivotal trials alone1. One of the more widely cited studies on drug costs used confidential survey data from ten multinational pharmaceutical firms and a random sample from a database of publicly available data10. In 2013, this study estimated the total pre-approval cost at $2.6 billion USD per approved new compound. This was a capitalised cost, and the addition of post-approval R&D costs increased this estimate to $2.87 billion (2013 USD). The out-of-pocket cost per approved new compound was reported at $1.395 billion, of which $965 million were clinical costs and the remaining $430 million were pre-clinical.

Another estimate reported the average cost to develop an asset at $1.296 billion in 20139. Furthermore, it reported that this cost had increased until 2019 at $2.431 billion and had since decreased to $2.376 billion in 2020 and $2.006 billion in 2021. While comparable to the previous out-of-pocket estimate for 2013, this study does not state whether their estimates are out-of-pocket or capitalised, making it difficult to meaningfully compare these estimates.

Figure 1: Recent cost estimates for drug development per approved new compound. “Clinical only” costs include only the costs of phase 0-III clinical trials, while “full” costs include pre-clinical costs. The colour of each bubble indicates the study, while bubble size indicated relative cost. A dashed border indicated the study used private data for their estimations, while a solid border indicates the study utilised publicly available data. Figure represents studies 9, 10, 12, 13 and 17 from the reference list in this report.

Publicly available data of 63 FDA-approved new biologics from 2009-2018 was used to estimate the capitalised (at 10.5%) R&D investment to bring a new drug to market at median of $985.3 million and a mean of $1.3359 billion (inflation adjusted to 2018 USD)12. These data were mostly accessible from smaller firms, smaller trials, first-in-class drugs, and further specific areas. The variation in estimated cost was, through sensitivity analysis, mostly explained by success/failure rates, preclinical expenditures, and cost of capital.

Publicly available data of 10 companies with no other drugs on the market in 2017 was used to estimate out-of-pocket costs for the development of a single cancer drug. This was reported at a median of $648 million and a mean of $719.8 million13. Capitalised costs were also reported using a 7% discount rate, with a median of $754.4 million and mean of $969.4 million. By focusing on data from companies without other drugs on the market, these estimates may better represent the development costs per new molecular entity (NME) for start-ups as the cost of failure of other drugs in the pipeline were included while any costs related to supporting existing on-market drugs were systematically impossible to include.

One study estimated the clinical costs per approved non-orphan drug at $291 million (out-of-pocket) and $412 million (capitalised 10.5%)17. The capitalised cost estimate increased to $489 million when only considering non-orphan NMEs. The difference between these estimates for clinical costs and the previously mentioned estimates for total development costs puts into perspective the amount

spent on pre-clinical trials and early drug development, with one studynoting their pre-clinical estimates comprised 32% of out-of-pocket and 42% of capitalised costs10.

Things to consider about cost estimates

The issue with these estimates is that there are so many differing factors affecting each study. This complicates cost-based pricing discussions, especially when R&D cost estimates can differ orders of magnitude apart. The methodologies used to calculate out-of-pocket costs differ between studies9,17, and the use of differing data sources (public data vs confidential surveys) seem to impact these estimates considerably.

There is also an issue with the transparency of data and methods from various sources in cost estimates. Some of this is a result of using confidential data, where some analyses are not available for public scrutiny8. This study in particular raised questions as estimates were stated without any information about the methodology or data used in the calculation of estimates. The use of confidential surveys of larger companies has also been criticised as the confidential data voluntarily submitted would have been submitted anonymously without independent verification12.

Due to the limited amount of comprehensive and published cost data17, many estimates have no option but to rely on using a limited data set and making some assumptions to arrive at a reasonable estimate. This includes a lack of transparent available data for randomised control trials, where one study reported that only 18% of FDA-approved drugs had publicly available cost data18. Therefore, there is a need for transparent and replicable data in this field to allow for more plausible cost estimates to be made, which in turn could be used to support budget planning and help trial sustainability18,26.

Despite the issues between studies, the findings within each study can be used to gather an idea of trends, cost drivers, and costs specific to company/drug types. For example, studies suggest an increasing overall cost of drug development from 1970 to peak in 201910, with a subsequent decrease in 2020 and 20219.

For a full list of references used in this article, please see the main report here: https://anatomisebiostats.com/biostatistics-blog/how-much-does-developing-a-novel-therapeutic-cost-factors-affecting-drug-development-costs-in-the-pharma-industry-a-mini-report/

How much does developing a novel therapeutic cost? – Factors Affecting Drug Development Costs across the Pharma Industry: A mini-Report

Introduction

Data evaluating the costs associated with developing novel therapeutics within the pharmaceutical industry can be used to identify trends over time and can inform more accurate budgeting for future research projects. However, the cost to develop a drug therapeutic is difficult to accurately evaluate, resulting in varying estimates ranging from hundreds of millions to billions of US dollars between studies. The high cost of drug development is not purely because of clinical trial expenses. Drug discovery, pre-clinical trials, and commercialisation also need to be factored into estimates of drug development costs.

There are limitations in trying to accurately assess these costs. The sheer number of factors that affect estimated and real costs means that studies often take a more specific approach. For example, costs will differ between large multinational companies with multiple candidates in their pipeline and start-ups/SMEs developing their first pharmaceutical. Due to the amount and quality of available data, many studies work mostly with data from larger multinational pharmaceutical companies with multiple molecules in the pipeline. When taken out of context, the “$2.6 billion USD cost for getting a single drug to market” can seem daunting for SMEs. It is very important to clarify what scale these cost estimates represent, but cost data from large pharma companies are still relevant for SMEs as they can used to infer costs for different scales of therapeutic development.

This mini-report includes what drives clinical trial costs, methods to reduce these costs, and then explores what can be learned from varying cost estimates.

What drives clinical trial costs?

There is an ongoing effort to streamline the clinical trial process to be more cost and time efficient. Several studies report on cost drivers of clinical trials, which should be considered when designing and budgeting a trial. Some of these drivers are described below:

Study size

Trial costs rise exponentially with an increasing study size, which some studies have found to be the single largest driver in trial costs1,2,3. There are several reasons for varying sample sizes between trials. For example, study size increases with trial phase progression as phases require different study sizes based on the number of patients needed to establish the safety and/or effectiveness of a treatment. Failure to recruit sufficient patients can result in trial delays which also increases costs4.

Trial site visits

A large study size is also correlated with a larger overall number of patient visits during a trial, which is associated with a significant increase in total trial costs2,3. Trial clinic visits are necessary for patient screening, treatment and treatment assessment but include significant costs for staff, site hosting, equipment, treatment, and in some cases reimbursement for patient travel costs. The number of trial site visits per patient varies between trials where more visits may indicate longer and/or more intense treatment sessions. One estimate for the number of trial visits per person was a median of 11 in a phase III trial, with $2 million added to estimated trial costs for every +1 to the median2.

Number & location of clinical trial sites

A higher number of clinical trial study sites has been associated with significant increase in total trial cost3. This is a result of increased site costs, as well as associated staffing and equipment costs. These will vary with the size of each site, where larger trials with more patients often use more sites or larger sites.

Due to the lower cost and shorter timelines of overseas clinical research5,6, there has been a shift to the globalisation of trials, with only 43% of study sites in US FDA-approved pivotal trials being in North America7. In fact, 71% of these trials had sites in lower cost regions where median regional costs were 49%-97% of site costs in North America. Most patients in these trials were either in North America (39.7%), Western Europe (21%), or Central Europe (20.4%).

Median cost per regional site as a percentage of North American median cost for comparison.

However, trials can face increased difficulties in managing and coordinating multiple sites across different regions, with concerns of adherence to the ethical and scientific regulations of the trial centre’s region5,6. Some studies have reported that multiregional trials are associated with a significant increase in total trial costs, especially those with sites in emerging markets3. It is unclear if this reported increase is a result of lower site efficiency, multiregional management costs, or outsourcing being more common among larger trials.

Clinical Trial duration

Longer trial duration has been associated with a significant increase in total trial costs3,4, where many studies have estimated the clinical period between 6-8 years8,9,10,11,12,13. Longer trials are sometimes necessary, such as in evaluating the safety and efficacy of long-term drug use in the management of chronic and degenerative disease. Otherwise, delays to starting up a trial contribute to longer trials, where delays can consume budget and diminish the relevance of research4. Such delays may occur as a result of site complications or poor patient accrual.

Another aspect to consider is that the longer it takes to get a therapeutic to market (as impacted by longer trials), the longer the wait is before a return of investment is seen by both the research organisation and investors. The period from development to on-market, often referred to as cycle time, can drive costs per therapeutic as interest based on the industry’s cost of capital can be applied to investments.

Therapeutic area under investigation

The cost to develop a therapeutic is also dependent on the therapeutic area, where some areas such as oncology and cardiovascular treatments are more cost intensive compared with others1,2,5,6,12,14. This is in part due to variation in treatment intensity, from low intensity treatments such as skin creams to high intensity treatments such as multiple infusions of high-cost anti-cancer drugs2. An estimate for the highest mean cost for pivotal trials per therapeutic area was $157.2M in cardiovascular trials compared to $45.4M in oncology, and a lowest of $20.8M in endocrine, metabolic, and respiratory disease trials1. This was compared to an overall median of $19M. Clinical

trial costs per therapeutic area also varied by clinical trial phase. For example, trials in pain and anaesthesia have been found to have the lowest average cost of a phase I study while having the highest average cost of a phase III study6.

It is important to note that some therapeutic areas will have far lower per patient costs when compared to others and are not always indicative of total trial costs. For example, infectious disease trials generally have larger sample sizes which will lead to relatively low per patient costs, whereas trials for rare disease treatment are often limited to smaller sample sizes with relatively high per patient costs. Despite this, trials for rare disease are estimated to have significantly lower drug to market costs.

Drug type being evaluated

As mentioned in the therapeutic areas section above, treatments may vary in intensity from skin creams to multiple rounds of treatment with several anti-cancer drugs. This can drive total trial costs due to additional manufacturing and the need for specially trained staff to administer treatments.

In the case of vaccine development, phase III/pivotal trials for vaccine efficacy can be very difficult to run unless there are ongoing epidemics for the targeted infectious disease. Therefore, some cost estimates of vaccine development include from the pre-clinical stages to the end of phase IIa, with the average cost for one approved vaccine estimated at $319-469 million USD in 201815.

Study design & trial control type used

Phase III trial costs vary based on the type of control group used in the trial1. Uncontrolled trials were the least expensive with an estimated mean of $13.5 million per trial. Placebo controlled trials had an estimated mean of $28.8 million, and trials with active drug comparators had an estimated mean cost of $48.9 million. This dramatic increase in costs is in part due to manufacturing and staffing to administer a placebo or active drug. In addition, drug-controlled trials require more patients compared to placebo-controlled, which also requires more patients than uncontrolled trials2.

Reducing therapeutic development costs

Development costs can be reduced through several approaches. Many articles recommend improvements to operational efficiency and accrual, as well as deploying standardised trial management metrics4. This could include streamlining trial administration, hiring experienced trial staff, and ensuring ample patient recruitment to reduce delays in starting and carrying out a study.

Another way to reduce development costs can take place in the thorough planning of clinical trial design by a biostatistician, whether in-house or external. Statistics consulting throughout a trial can help to determine suitable early stopping conditions and the most appropriate sample size. Sample size calculation is particularly important as underestimation undermines experimental results, whereas overestimation leads to unnecessary costs. Statisticians can also be useful during the pre-clinical stage to audit R&D data to select the best available candidates, ensure accurate R&D data analysis, and avoid pursuing unsuccessful compounds.

Other ways to reduce development costs include the use of personalised medicine, clinical trial digitisation, and the integration of AI. Clinical trial digitisation would lead to the streamlining of clinical trial administration and would also allow for the integration of artificial intelligence into clinical trials. There have been many promising applications for AI in clinical trials, including the use of electronic health records to enhance the enrolment and monitoring of patients, and the potential use of AI in trial diagnostics. More information about this topic can be found in our blog “Emerging use-cases for AI in clinical trials”.

For more information on the methodology by which pharmaceutical development and clinical trials costs are estimated and what data has been used please see the article: https://anatomisebiostats.com/biostatistics-blog/estimating-the-costs-associated-with-novel-pharmaceutical-development-methods-and-limitations/

Cost breakdown in more detail: How is a clinical trial budget spent?

Clinical trial costs can be broken down and divided into several categories, such as staff and non-staff costs. In a sample of phase III studies, personnel costs were found to be the single largest component of trial costs, consisting of 37% of the total, whereas outsourcing costs made up 22%, grants and contracting costs at 21%, and other expenses at 21%3.

From a CRO’s perspective, there are many factors that are considered in the cost of a pivotal trial quotation, including regulatory affairs, site costs, management costs, the cost of statistics and medical writing, and pass-through costs27. Another analysis of clinical trial cost factors determined clinical procedure costs made up 15-22% of the total budget, with administrative staff costs at 11-29%, site monitoring costs at 9-14%, site retention costs at 9-16%, and central laboratory costs at 4-12%5,6. In a study of multinational trials, 66% of total estimated trial costs were spent on regional tasks, of which 53.3% was used in trial sites and the remainder on other management7.

Therapeutic areas and shifting trends

Therapeutic area had previously been mentioned as a cost driver of trials due to differences in sample sizes and/or treatment intensity. It is however worth mentioning that, in 2013, the largest number of US industry-sponsored clinical trials were in oncology (2,560/6,199 active clinical trials with 215,176/1,148,340 patients enrolled)4,14. More recently, there has been a shift to infectious disease trials, in part due to the needed COVID-19 trials9.

Clinical trial phases

Due to the expanding sample size as a trial progresses, average costs per phase increase from phase I through III. Median costs per phase were estimated in 2016 at $3.4 million for phase I, $8.6 million for phase II, and $21.4 million for phase III3. Estimations of costs per patient were similarly most expensive in phase III at $42,000, followed by phase II at $40,000 and phase I at $38,50014. The combination of an increasing sample size and increasing per patient costs per phase leads to the drastic increase in phase costs with trial progression.

In addition, studies may have multiple phase III trials, meaning the median estimated cost of phase III trials per approved drug is higher than per trial costs ($48 million and $19 million respectively)2. Multiple phase III trials can be used to better support marketing approval or can be used for therapeutics which seek approval for combination/adjuvant therapy.

There are fewer cost data analyses available on phase 0 and phase IV on clinical trials. Others report that average Phase IV costs are equivalent to Phase III but much more variable5,6.

Orphan drugs

Drugs developed for the treatment of rare diseases are often referred to as orphan drugs. Orphan drugs have been estimated to have lower clinical costs per approved drug, where capitalised costs per non-orphan and orphan drugs were $412 million and $291 million respectively17. This is in part due to the limit to sample size imposed upon orphan drug trials by the rarity of the target disease and the higher success rate for each compound. However, orphan drug trials are often longer when compared to non-orphan drug trials, with an average study duration of 1417 days and 774 days respectively.

NMEs

New molecular entities (NMEs) are drugs which do not contain any previously approved active molecules. Both clinical and total costs of NMEs are estimated to be higher when compared to next in class drugs13,17. NMEs are thought to be more expensive to develop due to the increased amount of pre-clinical research to determine the activity of a new molecule and the increased intensity of clinical research to prove safety/efficacy and reach approval.

Conclusion & take-aways

There is no one answer to the cost of drug or device development, as it varies considerably by several cost drivers including study size, therapeutic area, and trial duration. Estimates of total drug development costs per approved new compound have ranged from $754 million12 to $2.6 billion10 USD over the past 10 years. These estimates do not only differ based on the data used, but also due to methodological differences between studies. The limited availability of comprehensive cost data for approved drugs also means that many studies rely on limited data sets and must make assumptions to arrive at a reasonable estimate.

There are still multiple practical ways that can be used to reduce study costs, including expert trial design planning by statisticians, implementation of biomarker-guided trials to reduce the risk of failure, AI integration and digitisation of trials, improving operational efficiency, improving accrual, and introducing standardised trial management metrics.

References

Moore T, Zhang H, Anderson G, Alexander G. Estimated Costs of Pivotal Trials for Novel Therapeutic Agents Approved by the US Food and Drug Administration, 2015-2016. JAMA Internal Medicine. 2018;178(11):1451-1457.

.1 Moore T, Zhang H, Anderson G, Alexander G. Estimated Costs of Pivotal Trials for Novel Therapeutic Agents Approved by the US Food and Drug Administration, 2015-2016. JAMA Internal Medicine. 2018;178(11):1451-1457.

2. Moore T, Heyward J, Anderson G, Alexander G. Variation in the estimated costs of pivotal clinical benefit trials supporting the US approval of new therapeutic agents, 2015–2017: a cross-sectional study. BMJ Open. 2020;10(6):e038863.

3. Martin L, Hutchens M, Hawkins C, Radnov A. How much do clinical trials cost?. Nature Reviews Drug Discovery. 2017;16(6):381-382.

4. Bentley C, Cressman S, van der Hoek K, Arts K, Dancey J, Peacock S. Conducting clinical trials—costs, impacts, and the value of clinical trials networks: A scoping review. Clinical Trials. 2019;16(2):183-193.

5. Sertkaya A, Birkenbach A, Berlind A, Eyraud J. Examination of Clinical Trial Costs and Barriers for Drug Development [Internet]. ASPE; 2014. Available from: https://aspe.hhs.gov/reports/examination-clinical-trial-costs-barriers-drug-development-0

6. Sertkaya A, Wong H, Jessup A, Beleche T. Key cost drivers of pharmaceutical clinical trials in the United States. Clinical Trials. 2016;13(2):117-126.

7. Qiao Y, Alexander G, Moore T. Globalization of clinical trials: Variation in estimated regional costs of pivotal trials, 2015–2016. Clinical Trials. 2019;16(3):329-333.

8. Monitor Deloitte. Early Value Assessment: Optimising the upside value potential of your asset [Internet]. Deloitte; 2020 p. 1-14. Available from: https://www2.deloitte.com/content/dam/Deloitte/be/Documents/life-sciences-health-care/Deloitte%20Belgium_Early%20Value%20Assessment.pdf

9. May E, Taylor K, Cruz M, Shah S, Miranda W. Nurturing growth: Measuring the return from pharmaceutical innovation 2021 [Internet]. Deloitte; 2022 p. 1-28. Available from: https://www2.deloitte.com/content/dam/Deloitte/uk/Documents/life-sciences-health-care/Measuring-the-return-of-pharmaceutical-innovation-2021-Deloitte.pdf

10. DiMasi J, Grabowski H, Hansen R. Innovation in the pharmaceutical industry: New estimates of R&D costs. Journal of Health Economics. 2016;47:20-33.

11. Farid S, Baron M, Stamatis C, Nie W, Coffman J. Benchmarking biopharmaceutical process development and manufacturing cost contributions to R&D. mAbs. 2020;12(1):e1754999.

12. Wouters O, McKee M, Luyten J. Estimated Research and Development Investment Needed to Bring a New Medicine to Market, 2009-2018. JAMA. 2020;323(9):844-853.

13. Prasad V, Mailankody S. Research and Development Spending to Bring a Single Cancer Drug to Market and Revenues After Approval. JAMA Internal Medicine. 2017;177(11):1569-1575.

14. Battelle Technology Partnership Practice. Biopharmaceutical Industry-Sponsored Clinical Trials: Impact on State Economies [Internet]. Pharmaceutical Research and Manufacturers of America; 2015. Available from: http://phrma-docs.phrma.org/sites/default/files/pdf/biopharmaceutical-industry-sponsored-clinical-trials-impact-on-state-economies.pdf

15. Gouglas D, Thanh Le T, Henderson K, Kaloudis A, Danielsen T, Hammersland N et al. Estimating the cost of vaccine development against epidemic infectious diseases: a cost minimisation study. The Lancet Global Health. 2018;6(12):e1386-e1396.
16. Hind D, Reeves B, Bathers S, Bray C, Corkhill A, Hayward C et al. Comparative costs and activity from a sample of UK clinical trials units. Trials. 2017;18(1).

17.Jayasundara K, Hollis A, Krahn M, Mamdani M, Hoch J, Grootendorst P. Estimating the clinical cost of drug development for orphan versus non-orphan drugs. Orphanet Journal of Rare Diseases. 2019;14(1).

19. Speich B, von Niederhäusern B, Schur N, Hemkens L, Fürst T, Bhatnagar N et al. Systematic review on costs and resource use of randomized clinical trials shows a lack of transparent and comprehensive data. Journal of Clinical Epidemiology. 2018;96:1-11.

20. Light D, Warburton R. Demythologizing the high costs of pharmaceutical research. BioSocieties. 2011;6(1):34-50.

21. Adams C, Brantner V. Estimating The Cost Of New Drug Development: Is It Really $802 Million?. Health Affairs. 2006;25(2):420-428.

22. Thomas D, Chancellor D, Micklus A, LaFever S, Hay M, Chaudhuri S et al. Clinical Development Success Rates and Contributing Factors 2011–2020 [Internet]. BIO|QLS Advisors|Informa UK; 2021. Available from: https://pharmaintelligence.informa.com/~/media/informa-shop-window/pharma/2021/files/reports/2021-clinical-development-success-rates-2011-2020-v17.pdf

23. Wong C, Siah K, Lo A. Estimation of clinical trial success rates and related parameters. Biostatistics. 2019;20(2):273-286.
24. Chit A, Chit A, Papadimitropoulos M, Krahn M, Parker J, Grootendorst P. The Opportunity Cost of Capital: Development of New Pharmaceuticals. INQUIRY: The Journal of Health Care Organization, Provision, and Financing. 2015;52:1-5.
25. Harrington, S.E. Cost of Capital for Pharmaceutical, Biotechnology, and Medical Device Firms. In Danzon, P.M. & Nicholson, S. (Eds.), The Oxford Handbook of the Economics of the Biopharmaceutical Industry, (pp. 75-99). New York: Oxford University Press. 2012.
26. Zhuang J, Liang Z, Lin T, De Guzman F. Theory and Practice in the Choice of Social Discount Rate for Cost-Benefit Analysis: A Survey [Internet]. Manila, Philippines: Asian Development Bank; 2007. Available from: https://www.adb.org/sites/default/files/publication/28360/wp094.pdf
27. Rennane S, Baker L, Mulcahy A. Estimating the Cost of Industry Investment in Drug Research and Development: A Review of Methods and Results. INQUIRY: The Journal of Health Care Organization, Provision, and Financing. 2021;58:1-11.
28. Ledesma P. How Much Does a Clinical Trial Cost? [Internet]. Sofpromed. 2020 [cited 26 June 2022]. Available from: https://www.sofpromed.com/how-much-does-a-clinical-trial-cost


The Role of Precision Medicine in Drug Development and Clinical Trials

With the help of precision medicine, or personalised medicine, modern medicine has moved away from a ‘one size fits all’ approach to treating disease and towards therapeutic approaches that are tailored to individuals and subgroups. These treatments are designed to be more efficacious due to targeting population subgroups based on their genetic or molecular nuances, rather than operating on the assumption that all bodies function and respond the same way and to the same degree to a given treatment. Molecular knowledge can now be utilised to tailor treatment to the patient at the correct dosage and time point, usually with the aid of pharmacogenomic approaches and molecular biomarkers.

Information about an individual’s genetic makeup, such as genetic variants that may influence treatment efficacy, toxicity, and adverse events can help to determine how patients will respond to a certain treatment. In addition to genomic, recent technological advances have led to the identification of many transcriptomic and proteomic biomarkers. This knowledge is useful in all stages of therapeutic development and can influence both the design of the therapeutic itself and of the clinical trial.


Drug Development

Inter-individual variations in drug response can result from polymorphisms in drug metabolizing enzymes. Thorough examination of gene expression and mutations in disease populations can lead to the identification of distinct disease subpopulations that share certain characteristics.  Further exploration of these genes and their interactions can uncover possible drug target genes for the treatment of a disease subpopulation.

Furthermore, an awareness of genetic variation in disease subpopulations means that the involved pathways and components can be more accurately recreated in pre-clinical studies. Bringing the gap between animal and human toxicity findings allows for more representative disease models. This allows variability in treatment response and optimal dosage to be explored more precisely.


Clinical Trial Design

Originally, clinical trials were designed to evaluate differences between novel treatments and standard treatments or controls, not among individual responses in treated groups. As a result, a therapeutic that was very effective in only a subgroup of the trial population may not have produced significant results and a therapeutic that caused adverse events in only a small subgroup could have been deemed too toxic for overall use.

The goal of clinical trials to gain regulatory approval remains unchanged. With the emergence of precision medicine come biomarker-driven trials that include patient subgroups in their design. Master protocols for trials enable the application of one treatment to multiple diseases, or multiple treatments to one disease, allowing a trial to adapt during its course. This room for adaptation can reduce financial impact due to ineffective treatments being abandoned earlier and targeting the most suitable groups. Incorporating a diagnostic assay in trial design can offer multiple advantages and prevent research from straying in the wrong direction.

Targeted therapies can be tested in the most appropriate patient groups likely to benefit by biomarker testing of patients prior to clinical trial participation. Screening patients for those more likely to respond well to treatment gives a greater estimate of treatment effect in the subgroup.  This increases the likelihood of demonstrating efficacy in a clinical trial. It also reduces the size of the sample population required to see statistically significant results, which can speed up the process.

Identifying responders before enrolment in such a manner minimises the number of exposed patients who would not benefit from treatment. Decreasing the risk of exposing non-responders to potential adverse events can improve the benefit/risk analysis.

Patient stratification is another aspect of trial design that utilises patient’s molecular biomarker profiles. Stratifying trial participants into subgroups can classify disease subtypes. Particularly in oncology, genomic approaches can guide the stratification of patients by their tumour mutations. It is notably useful in umbrella, basket, and platform trials and can reduce the financial impact by allowing for adaptive trials.

Umbrella trials test multiple targeted therapeutics in different biomarker cohorts of a single disease. Basket trials, on the other hand, test one or more targeted therapeutics in a patient cohort with matched biomarkers. Platform trials have a randomised structure and allow the evaluation of multiple targeted therapeutics in multiple biomarker-selected populations.


Application in developed therapeutics

While precision medicine approaches are most beneficial when included throughout the drug development process, their application can also improve or salvage existing treatments and prevent a clinical trial from failing. For example, a developed drug may cause severe adverse events in a small disease subpopulation.  Upon investigation it is found that the drug has a secondary target, which is only present in that subgroup.  With this knowledge, patients can be screened for presence/absence of the safety biomarker and intervention with said drug can be avoided in that subgroup while continued in the remaining population.

Alternatively, a drug may have clinically meaningful results in only a small number of patients. The responsive subgroup can be explored for potential biomarkers associated with degree of responsiveness to treatment. The clinical trial can then resume with a focus on patients likely to respond well to the therapeutic.


Response Monitoring

Throughout and after a clinical trial, biomarkers can be used as a means of observing patient response to intervention, and account for variability in response. Safety and efficacy monitoring markers will reveal individual cases where treatment is working effectively or needs to be halted due to adverse events. For example, a cancer-related gene mutation or protein detected in blood may no longer be present after successful treatment has been administered, showing that the treatment has worked.

Identified responders or non-responders can be further stratified into subgroups and studied.  Genomic information can aid in the understanding of outliers and changes to treatment response. This will contribute to disease and therapeutic understanding, so that the right patients can be given the right dose, getting the most benefit out of treatment.


Challenges of Precision Medicine

It should be noted that the development of a targeted therapy requires the right data, both for the identification of the drug target and suitable patients. Molecular data from disease populations in previous studies may not always be available during drug development. If available, it may not be the correct type of data or generated by the most appropriate assay. Developing a targeted therapy is not possible without suitable data to understand disease mechanisms and identify putative drug targets.

Biomarker-driven therapies require genetic tests and companion diagnostics to identify and distinguish suitable patients. Incorporating diagnostic methods in a clinical trial is an added cost and the process can be burdensome as it can make participant recruitment harder. Clinical intervention according to the results of stratification should also be well-defined before a trial phase commences.
 


References
Di Liello, R., Piccirillo, M., Arenare, L., Gargiulo, P., Schettino, C., Gravina, A. and Perrone, F., 2021. Master Protocols for Precision Medicine in Oncology: Overcoming Methodology of Randomized Clinical Trials. Life, 11(11), p.1253.
Dugger, S., Platt, A. and Goldstein, D., 2017. Drug development in the era of precision medicine. Nature Reviews Drug Discovery, 17(3), pp.183-196.
Mirsadeghi, S. and Larijani, B., 2017. Personalized Medicine: Pharmacogenomics and Drug Development. Acta Med Iran, 55(3), pp.150-165.
Woodcock, J., 2007. The Prospects for “Personalized Medicine” in Drug Development and Drug Therapy. Clinical Pharmacology & Therapeutics, 81(2), pp.164-169.
 

Clinical Trial Phases in Drug Development


The development of new drugs starts far before they are even seen in clinical trials. The discovery of multiple candidate drugs occur early on in the development process, often as a result of new information about how a disease functions, large-scale screening of small molecules, or the release of a new technology.

After a promising drug has been found, pre-clinical studies can be performed. A pre-clinical study for a new drug is used to determine important information about toxicity and suitable dosage amounts. These studies can be in vitro (in cell culture) and/or in vivo (in animal models) and determine whether a treatment will continue to the clinical trials stage.

Clinical trials test whether these experimental treatments are safe for use in humans, and whether they are more effective in treating or preventing a disease when compared to existing treatments. Clinical trials consist of several stages, called phases, where each phase is focused on answering a different clinical question: Progression of a treatment to the next phase requires the study to meet several parameters to ensure a treatment’s safety or efficacy.

  • Phase 0: Is the new treatment safe to use in humans in small doses?
  • Phase I: Is the new treatment safe to use in humans in therapeutic doses?
  • Phase II: Is the new treatment effective in humans?
  • Phase III: Is the new treatment more effective than existing treatments?
  • Phase IV: Does the new treatment remain safe and effective post-market?
Key phases of a pharmaceutical clinical trial

Phase 0: Small dose safety

Phase 0 studies can help to streamline the other clinical trial phases. Phase 0 consists of giving a few patients small, sub-therapeutic doses of the new treatment. This is to make sure that the new treatment behaves as expected by researchers and isn’t harmful to humans prior to using higher doses in phase I trials.

Phase I: Therapeutic dose safety

Phase I studies evaluate the safety of various doses of the new treatment in humans. This takes several months with typically around 20-80 healthy volunteers. In some cases, such as in anti-cancer drug trials, the study participants are patients with the targeted cancer type. A treatment may not pass phase I if the treatment leads to any serious adverse events.

Initial dosages in phase I studies can be informed based on data obtained during pre-clinical animal studies, and adjustments can be made to investigate the treatment’s side effect profile and develop an optimal dosing program. This could also include comparing different methods of giving a drug to patients (e.g., oral, intravenous etc.).

Phase II: Treatment efficacy

After passing phase I trials and having proven safety in humans, a new treatment advances to phase II studies designed to assess whether it may prevent or treat a disease. This phase can take between several months to 2 years, testing the new treatment in up to several hundred patients with the disease. Using a larger number of patients over a longer time period provides researchers with additional safety and effectiveness data, which is essential for the design of phase III trials.

To further test safety and efficacy, it is common to have a control group that receives either a placebo (a harmless pill or injection without the new treatment) or other current treatment (in trials where the disease is fatal unless treated e.g., cancer).

Phase III: Comparing to current treatments

Phase III studies are the last stage of a clinical trial before a new treatment can be approved for market use. The primary focus of a phase III study is to compare the safety and efficacy of a new treatment with current, existing treatments in patients with the target disease. Anywhere from several hundred to 3,000 patients may be included in a phase III study for between 1 to 4 years. Due to the scale of this phase, long-term or rare side effects are more likely to be uncovered.

Phase III studies are often randomised control trials, where patients will be randomly designated to different treatment groups. These groups may receive placebo, a current treatment (control group), the new treatment, or variations of the new treatment (e.g., different drug combinations). Randomised control trials are often double-blinded, where both the patient and the clinician administering their treatment do not know which treatment group they are assigned to.

A new treatment may continue to market and phase IV trials if the results prove it is as safe and effective as an existing treatment.

Phase IV: Post-market surveillance

If a new treatment passes phase III and is approved by the MHRA, FDA, or other national regulatory agency, it can be put to market. Phase IV is carried out in the post-market surveillance of the new treatment to keep updated on any emerging or long-term safety and efficacy concerns. This may include rare or long-term adverse side effects that were not yet discovered, or long-term analyses to see if the new treatment improves the life expectancy of a patient after recovery from disease.

Summary

Clinical trials are ultimately designed to mitigate risk. This includes the risk to the safety of trial participants by limiting the use of potentially unsafe treatments to small doses in a small number of patients before scaling up to testing therapeutic dose safety. Risk mitigation is not only for patient safety but also for preventing financial misspending as a treatment that is deemed unsafe in phase 0 would not proceed to the later, more costly clinical trial phases.

Not all clinical trials are the same, however, as each trial will have a different disease and treatment context. Trials for medical devices are somewhat different from pharmaceutical trials (for more information about the differences between medical device and pharma trials, click here). In addition, while sample sizes expand with phase progression, the required sample size for each trial and each phase is dependent on several factors including disease context (a rare disease may require lower sample sizes), patient availability (location of trial), trial budget and effect size. The sample size values mentioned earlier in this blog are purely indications of what each phase may use (for more information on how a biostatistician determines a suitable sample size, click here).

References

https://www.fda.gov/patients/drug-development-process/step-3-clinical-research

https://www.healthline.com/health/clinical-trial-phases

Medical Device Clinical Trials vs Pharmaceutical Clinical Trials – What’s the Difference?

Medical devices and drugs share the same goal – to safely improve the health of patients. Despite this, substantial differences can be observed between the two. Principally, drugs interact with biochemical pathways in human bodies while medical devices can encompass a wide range of different actions and reactions, for example, heat, radiation (Taylor and Iglesias, 2009). Additionally, medical devices encompass not only therapeutic devices but diagnostic devices, as well (Stauffer, 2020).

More specifically medical device categories can include therapeutic and surgical devices, patient monitoring, diagnostic and medical imaging devices, among others; making it a very heterogeneous area (Stauffer, 2020). As such, medical device research spills over into many different fields of healthcare services and manufacturing. This research is mostly undertaken by SME’s ( small to medium enterprises) instead of larger well-established companies as is more predominantly the case with pharmaceutical research. SME’s and start-ups undertake the majority of the early stage device development, particularly where any new class of medical device is concerned, whereas the larger firms get involved in later stages of the testing process (Taylor and Iglesias, 2009).

Classification criteria for medical devices

There are strict regulations that researchers and developers need to follow, which includes general device classification criteria. This classification criterion consists of three classes of medical devices, the higher class medical device the stricter regulatory controls are for the medical device. 

  • Class I, typically do not require premarket notifications
  • Class II,  require premarket notifications
  • Class III, require premarket approval

Food and Drug Administration (FDA)

Drug licensing and market access approval by the Food and Drug Administration (FDA) and international equivalents require manufacturers to undertake phase II and III randomised controlled trials in order to provide the regulator with evidence of their drug’s efficacy and safety (Taylor and Iglesias, 2009).

Key stages of medical device clinical trials

In general medical device clinical trials are smaller than drug trials and usually start with feasibility study. This provides a limited clinical evaluation of the device. Next a pivotal trial is conducted to demonstrate the device in question is safe and effective (Stauffer, 2020).

Overall the medical device trials can be considered to have three stages:

  • Feasibility study,
  • Pivotal study to determine if the device is safe and effective,
  • Post-market study to analyse the long-term effectiveness of the device.

Clinical evaluation for medical devices

Clinical evaluation is an ongoing process conducted throughout the life cycle of a medical device. It is first performed during the development of a medical device in order to identify data that need to be generated for regulatory purposes and will inform if a new device clinical investigation is necessary. It is then repeated periodically as new safety, clinical performance and/or effectiveness information about the medical device is obtained during its use.(International Medical Device Regulators Forum, 2019)

During the evaluative process, a distinction must be made between device types – diagnostic or therapeutic. The criteria for diagnostic technology evaluations are usually divided into four groups:

  • technical capacity
  • diagnostic accuracy
  • diagnostic and therapeutic impact
  • patient outcome

The importance of evaluation

Evaluations provide important information about a device and can indicate the possible risks and complications. The main measures of diagnostic performance are sensitivity and specificity. Based on the results of the clinical investigation the intervention may be approved for the market. When placing a medical device on the market, the manufacturer must have demonstrated through the use of appropriate conformity assessment procedures that the medical device complies with the Essential Principles of Safety and Performance of Medical Devices(International Medical Device Regulators Forum, 2019).The information on effectiveness can be observed by conducting experimental or observational studies.

Post-market surveillance

Manufacturers are expected to implement and maintain surveillance programs that routinely monitor the safety, clinical performance and/or effectiveness of the medical device as part of their Quality Management System (International Medical Device Regulators Forum, 2019). The scope and nature of such post market surveillance should be appropriate to the medical device and its intended use. Using data generated from such programs (e.g. safety reports, including adverse event reports; results from published literature, any further clinical investigations), a manufacturer should periodically review performance, safety and the benefit-risk assessment for the medical device through a clinical evaluation, and update the clinical evidence accordingly.

The use of databases in medical device clinical trials

The variations in the available evidence-base for devices means that, unlike with drugs, medical devices will typically require the consideration and analysis of data from observational studies in ascertaining their clinical and cost-effectiveness. Using modern observational databases has advantages because these databases represent continuous monitoring of the device in real-life practice, including the outcome (Maresova et al., 2020).

Bayesian methods as an alternative framework for evaluation

Bayesian methods for the analysis of trial data have been proposed as an alternative framework for evaluation within the FDA’s Center for Devices and Radiological Health. These methods provide flexibility and may make them particularly well suited to address many of the issues associated with the assessment of clinical and economic evidence on medical devices, for example, learning effects and lack of head-to-head comparisons between different devices.

Use of placebo in medical vs pharmaceutical trials

An additional key difference between drug and medical device trials are that use of placebo in medical device trials are rare. If placebo is used in a trial for surgical / implanted devices  it would usually be a sham surgery or implantation of a sham device (Taylor and Iglesias, 2009). Sham procedures are high risk and may be considered unethical. Without this kind of control, however, there is in many cases no sure way of knowing whether the device is providing real clinical benefit or if the benefit experienced is due to the placebo effect. 

Conclusion

            In conclusion, there are many similarities between medical device and pharmaceutical clinical trials, but there are also some really important differences that one should not miss:

  1.  In general medical device clinical trials are smaller than drug trials.
  2.  The research is mostly undertaken by SME’s ( small to medium enterprises) instead of big well-known companies
  3. Drugs interact with biochemical pathways in human bodies whereas medical devices use a wide range of different actions and reactions, for example, heat, radiation.
  4. Medical devices can be used for not only diagnostic purposes but therapeutical purposes as well.
  5.  The use of placebo in medical device trials are rare in comparison to pharmaceutical clinical trials.

References:

Bokai WANG, C., 2017. Comparisons of Superiority, Non-inferiority, and Equivalence Trials. [online] PubMed Central (PMC). Available at: <https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5925592/> [Accessed 28 February 2022].

Chen, M., Ibrahim, J., Lam, P., Yu, A. and Zhang, Y., 2011. Bayesian Design of Noninferiority Trials for Medical Devices Using Historical Data. Biometrics, 67(3), pp.1163-1170.

E, L., 2008. Superiority, equivalence, and non-inferiority trials. [online] PubMed. Available at: <https://pubmed.ncbi.nlm.nih.gov/18537788/> [Accessed 28 February 2022].

Gubbiotti, S., 2008. Bayesian Methods for Sample Size Determination and their use in Clinical Trials. [online] Core.ac.uk. Available at: <https://core.ac.uk/download/pdf/74322247.pdf> [Accessed 28 February 2022].

U.S. Food and Drug Administration. 2010. Guidance for the Use of Bayesian Statistics in Medical Device Clinical. [online] Available at: <https://www.fda.gov/regulatory-information/search-fda-guidance-documents/guidance-use-bayesian-statistics-medical-device-clinical-trials> [Accessed 28 February 2022].

van Ravenzwaaij, D., Monden, R., Tendeiro, J. and Ioannidis, J., 2019. Bayes factors for superiority, non-inferiority, and equivalence designs. BMC Medical Research Methodology, 19(1).