CRF Design for Clinical Studies: The Distinct Roles of Data Managers vs Biostatisticians

In medtech clinical trials, the Case Report Form (CRF) is more than a tool for collecting data—it’s the backbone of the study. From capturing critical safety outcomes to evaluating device performance, a well-designed CRF ensures that the study’s goals are met efficiently and reliably.

Achieving this balance requires input from two key roles: the data manager and the biostatistician. While their contributions may overlap in some areas, these roles serve distinct and complementary purposes. Understanding how these professionals work together can help medtech sponsors avoid common pitfalls in CRF design and maximise the success of their trials.

The Data Manager’s Role in CRF Design

Data managers are experts in the operational and technical aspects of CRF design. Their role is to ensure that data collection is standardised, consistent, and compliant with relevant guidelines.

Key responsibilities of a data manager include:

• Formatting CRFs: Ensuring fields are user-friendly and compatible with electronic data capture (EDC) systems.
• Regulatory Compliance: Aligning CRFs with industry standards such as CDASH (Clinical Data Acquisition Standards Harmonization).
• Site Usability: Designing forms that facilitate accurate and consistent data entry across multiple trial sites.

For instance, a data manager might ensure that dropdown menus in the CRF prevent free-text responses, reducing the risk of inconsistencies. Their focus is on the practical and technical aspects of data collection.

The Biostatistician’s Role in CRF Design

Biostatisticians, on the other hand, approach CRF design from an analytical perspective. Their focus is on ensuring that the data collected aligns with the study’s endpoints and supports meaningful statistical analysis.

Key responsibilities of a biostatistician include:

• Aligning Data with Study Objectives: Defining the variables that need to be captured to evaluate the endpoints outlined in the Statistical Analysis Plan (SAP).
• Variable Definition: Ensuring that collected data supports statistical methods, such as properly coding categorical variables (e.g., mild/moderate/severe).
• Derived Metrics: Identifying composite or derived variables that must be pre-defined in the CRF to support downstream analysis.

For example, in a post-market study evaluating a vascular device, the biostatistician would ensure that the CRF captures restenosis rates in a format that allows calculation of primary patency—a key endpoint. Their input ensures that no critical data points are overlooked.

Why Both Roles Are Essential to MedTech Trials

Although data managers and biostatisticians work towards the same goal—collecting high-quality data—their approaches and expertise are fundamentally different. Collaboration between these roles is essential for creating CRFs that are both operationally feasible and analytically robust.

1. Preventing Data Gaps

Without biostatistician oversight, CRFs may fail to capture key variables required for endpoint evaluation. For example:

• In a stent study, a missing field for recording restenosis or target vessel occlusion could render the primary endpoint unanalysable.
• Failure to specify timepoints for data collection (e.g., 12-month vs. 60-month follow-up) may result in incomplete datasets for secondary analyses.

2. Ensuring Data Compatibility

While data managers ensure that CRFs meet technical and regulatory standards, biostatisticians ensure the data is analyzable. Misalignment in variable formats can lead to delays or errors during analysis. For instance:

• Categorical variables (e.g., adverse event severity) coded as free text at some sites and numeric values at others can complicate statistical programming.

3. Regulatory-Ready Analysis

In medtech trials, regulatory submissions rely heavily on robust statistical reporting. Biostatisticians ensure that CRFs are designed to collect all data necessary for generating high-quality, compliant analyses. For example:

• Derived metrics like cumulative incidence rates must be pre-defined in the CRF to avoid regulatory scrutiny over post-hoc adjustments.

Misconceptions About CRF Design in MedTech

A common misconception in medtech trials is that data managers can fully handle CRF design. While data managers are essential for operationalising CRFs, their expertise does not extend to defining the analytical framework needed to support endpoints and hypotheses.

The Risks of Excluding Biostatistician Input

When biostatisticians are excluded from CRF design:

• Key Variables May Be Missing: Critical fields for evaluating endpoints may be omitted.
• Data May Be Misaligned: Improperly coded variables can lead to delays during analysis or errors in reporting.
• Regulatory Challenges May Arise: Incomplete or improperly formatted data can result in regulatory delays or rejection.

By including biostatisticians early in the CRF design process, sponsors can avoid these risks and ensure their study remains on track.

Real-World Example: The Power of Collaboration

Consider a post-market surveillance study for a diagnostic device. The sponsor relies on CRFs to collect data on device performance across real-world clinical settings. Initially, the data manager designed the CRFs to focus on ease of use at the sites. However, the biostatistician identified a critical oversight: the CRFs did not include fields to track device calibration data, a key variable for assessing long-term performance trends. By collaborating, the data manager and biostatistician ensured the CRFs met both operational and analytical requirements, setting the stage for a successful regulatory submission.

Practical Steps for MedTech Sponsors

To ensure robust CRF design in your medtech trial, consider these steps:

1. Involve Biostatisticians Early: Engage your biostatistician during the CRF design phase to define variables and ensure alignment with study endpoints.
2. Foster Collaboration: Encourage close communication between data managers and biostatisticians to balance operational efficiency with analytical rigor.
3. Prioritise Regulatory Readiness: Design CRFs with regulatory requirements in mind to avoid costly delays during submission.

Final Thoughts

In medtech clinical trials, the success of your study depends on more than just collecting data—it depends on collecting the right data in the right way. Data managers and biostatisticians each bring unique expertise to CRF design, and their collaboration ensures that your trial is set up for operational efficiency, analytical validity, and regulatory success.

By recognising the complementary roles of these professionals, medtech sponsors can avoid common pitfalls and ensure their studies deliver meaningful, actionable results. If you’re planning a clinical trial and want to learn more about how to optimise CRF design, our team at Anatomise Biostats is here to help.

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.

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.

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.