02 Dec 2024

Biostatistics, Clinical Trials, Med-Tech & Medical Device, Pharma & Drug Development, Uncategorized

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:

Involve Biostatisticians Early: Engage your biostatistician during the CRF design phase to define variables and ensure alignment with study endpoints.
Foster Collaboration: Encourage close communication between data managers and biostatisticians to balance operational efficiency with analytical rigor.
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.

20 Oct 2024

By Anatomise Biostats

Biometrics & Clinical Trials Success:

Biostatistics, Clinical Trials, Med-Tech & Medical Device, Pharma & Drug Development, Uncategorized

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.

01 Apr 2024

By Anatomise Biostats

Fake vs Synthetic Data: What’s the difference?

Uncategorized

The ethical and accurate handling of data is paramount in the domain of clinical research. As the demand for data-driven clinical insights continues to grow, researchers face challenges in balancing the need for accuracy with the availability of data and the imperative to protect sensitive information. In situations where quality real patient data is not available, synthetic data can be the most reliable data source from which to derive predictive insights. Synthetic data can be more cost-effective and time-efficient in many cases than acquiring the equivalent real data.

Synthetic data must be differentiated from fake data. In recent years there has been much controversy concerning fake data detected in published journal articles which have previously passed peer review, particularly in an academic context. As one study is generally built upon assumptions formed by the results of another, this preponderance of fake data has really had a catastrophic impact on our ability to trust any published scientific research, regardless of whether the study at hand also contains fake data. It has become clear that the implementation of increased quality control standards for all published research needs to be prioritised.

While synthetic data is not without it’s own pitfalls, the key difference between synthetic and fake data lies in it’s purpose and authenticity. Synthetic data is designed to emulate real-world data for specific use cases, maintaining statistical properties without revealing actual (individual) information. On the other hand, fake data is typically fabricated and may not adhere to any real-world patterns or statistics.

In clinical research, the use of real patient data is fraught with privacy concerns and other ethical considerations. Accurate and consistent patient data can also be hard to come by for other reasons such as heterogeneous recording methods or insufficient disease populations. Synthetic data is emerging as a powerful solution to navigate these limitations. While accurate synthetic data is not a trivial thing to generate, researchers can harness advanced algorithms and models built by expert data scientists to generate synthetic datasets that faithfully mimic the statistical properties and patterns of real-world patient and other data. This allows researchers to simulate and predict relevant clinical outcomes in situations where real data is not readily available, and do so without compromising individual patient privacy.

A large proportion of machine learning models in an AI context are currently being trained on synthetic rather than real data. This is largely because using generative models to create synthetic data tends to be much faster and cheaper than collecting real-world-data. Real-world data can at times lack sufficient diversity to make insights and predictions truly generalisable.

Both the irresponsible use of synthetic data and the generation and application of fake data in academic, industry and clinical research settings can have severe consequences. Whether stemming from dishonesty or incompetence, the misuse of fake data or inaccurate synthetic data poses a threat to the integrity of scientific inquiry.

This following sections define and delineate between synthetic and fake data as well as summarise the key applications of synthetic data in clinical research as compared to the potential pitfalls associated with the unethical use of fake data.

Synthetic Data:

Synthetic data refers to artificially generated data that mimics the statistical properties and patterns of real-world data. It is created using various algorithms, models, or simulations to resemble authentic patient data as closely as possible. It may do so without containing any real-world identifying information about individual patients comprising the original patient sample from which it was derived.

Synthetic data can be used in situations where privacy, security, or confidentiality concerns make it challenging to access or use real patient data. It can also be used in cases where an insufficient volume of quality patient data is available or where existing data is too heterogeneous to draw accurate inferences, such as is typically the case with rare diseases. It can potentially be employed in product testing to create realistic scenarios without subjecting real patients to unnecessary risk.

3 key use cases for synthetic data in clinical research

1. Privacy Preservation:

– Synthetic data allows researchers to conduct analyses and develop statistical models without exposing sensitive patient information. This is particularly crucial in the healthcare and clinical research sectors, where maintaining patient confidentiality is a legal and ethical imperative.

2. Robust Testing Environments:

– Clinical trials and other experiments related to product testing or behavioural interventions often necessitate testing in various scenarios. Synthetic data provides a versatile and secure testing ground, enabling researchers to validate algorithms and methodologies without putting real patients at risk.

3. Data Augmentation for Limited Datasets:

– In situations where obtaining a large and diverse dataset is challenging, synthetic data serves as a valuable tool for augmenting existing datasets. This aids in the development of more robust models and generalisable findings. A data set can be made up of varying proportions of synthetic versus real-world data. For example, a real world data set may be fairly large but lack diversity on the one hand, or small and overly heterogeneous on the other. The methods of generating synthetic data to augment these respective data sets would differ in each case.

Fake Data:

Fake data typically refers to data that is intentionally fabricated or inaccurate due to improper data handling techniques. In situations of misuse it is usually combined with real study data to give misleading results.

Fake data can be used ethically for various purposes, such as placeholder values in a database during development, creating fictional scenarios for training or educational purposes, or generating data for scenarios where realism is not crucial. Unfortunately in the majority of notable academic and clinical cases it has been used with the deliberate intention to mislead by doctoring study results and thus poses a serious threat to the scientific community and the general public.

.There are three key concerns with fake data.

1. Academic Dishonesty:

– Some researchers may be tempted to fabricate data to support preconceived conclusions, meet publication deadlines or attain competitive research grants. After many high profile cases in recent years it has become apparent that this is a pervasive issue across academic and clinical research. This form of academic dishonesty undermines the foundation of scholarly pursuits and erodes the trust placed in research findings.

2. Mishaps and Ineptitude:

– Inexperienced researchers may inadvertently create fake data, whether due to poor data collection practices, computational errors, or other mishaps. This unintentional misuse can lead to inaccurate results, potentially rendering an entire body of research unreliable if it remains undetected.

3. Erosion of Trust and Reproducibility:

– The use of fake data contributes to the reproducibility crisis in scientific research. One study found that 70% of studies cannot be reproduced due to insufficient reporting of data and methods. When results cannot be independently verified, trust in the scientific process diminishes, hindering the advancement of knowledge. The addition of fake data into this scenario makes replication and thus verification of study results all the more challenging.

In an evolving clinical research landscape, the responsible and ethical use of data is paramount. Synthetic data stands out as a valuable tool in protecting privacy, advancing research, and addressing the challenges posed by sensitive information – assuming it is generated as accurately and responsibly as possible. On the other hand, the misuse of fake data undermines the integrity of scientific research, eroding trust and impeding the progress of knowledge and it’s real-world applications. It is important to stay vigilant against bias in data and employ stringent quality control in all data contexts of data handling.

02 Aug 2023

By Anatomise Biostats

The Role of Clinical-Translational Studies in Validation of Diagnostic Devices

Biostatistics, Med-Tech & Medical Device

Clinical-translational studies refer to research studies that bridge the gap between early-stage diagnostic development and real-world clinical application. In a diagnostics context these studies focus on translating promising diagnostic technologies from laboratory research (preclinical stage) to clinical practice, where they can be validated, assessed for clinical utility, and eventually integrated into routine healthcare settings.

The primary goal of clinical-translational studies for diagnostics is to evaluate the performance, accuracy, safety, and overall effectiveness of new diagnostic tests or devices in real-world patient populations. These studies play a critical role in determining whether the diagnostic technology can reliably detect specific diseases or conditions, guide treatment decisions, improve patient outcomes, and enhance the overall healthcare experience.

Key Characteristics of Clinical-Translational Studies for Diagnostics:

Validation of Diagnostic Accuracy:
In clinical-translational studies, diagnostic accuracy and reliability is rigorously validated. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) are assessed to determine how effectively the diagnostic test can identify true positive and true negative cases. These metrics provide essential insights into the precision and reliability of the test’s performance.

Clinical Utility Evaluation:
Beyond accuracy, clinical-translational studies focus on evaluating the clinical utility of the diagnostic technology. The impact of the test on patient management, treatment decisions, and overall healthcare outcomes is carefully assessed. Real-world data is analysed to understand how the test guides appropriate clinical actions and leads to improved patient outcomes. This evaluation helps stakeholders better assess the value of the diagnostic test in clinical practice.

Inclusion of Diverse Patient Populations:
Clinical-translational studies encompass a wide range of patient populations to ensure the generalisability of the diagnostic test’s results. Studies are designed to include patients with various demographics, medical histories, and disease severities, making the findings applicable to real-world scenarios. Robust statistical analyses are employed to identify potential variations in test performance across different patient groups, enhancing the diagnostic test’s inclusivity and practicality.

Comparative Analyses:
In certain cases, comparative analyses are conducted in clinical-translational studies to evaluate the performance of the new diagnostic technology against existing standard-of-care tests or reference standards. Differences in accuracy and clinical utility are quantified using statistical methods, enabling stakeholders to make informed decisions regarding the adoption of the new diagnostic test or device.

Use of Real-World Evidence:
Real-world evidence plays a pivotal role in clinical-translational studies. Data from routine clinical practice settings are collected to assess the test’s performance under authentic healthcare conditions. Advanced statistical techniques are employed to analyse real-world data, providing valuable insights into how the diagnostic test performs in real patient populations. This evidence informs the adoption and implementation of the test in clinical practice.

Compliance with Regulatory Guidelines:
Compliance with regulatory guidelines and standards is essential for the success of clinical-translational studies. Studies are designed and conducted following regulatory requirements set by health authorities, ensuring adherence to Good Clinical Practice (GCP) guidelines and ethical considerations to ensure data quality and to protect patient safety and privacy.

Conducting Longitudinal Studies:
For certain diagnostic technologies, particularly those used for monitoring or disease progression, longitudinal studies may be necessary. These studies are designed to assess the diagnostic device’s performance over time and identify potential variations or trends. Longitudinal analyses enable researchers to understand how the diagnostic test performs in the context of disease progression and treatment response.

Interdisciplinary Collaboration:
Clinical-translational studies involve collaboration among diverse stakeholders, such as clinicians, biostatisticians, regulatory experts, and industry partners. Biostatisticians play a pivotal role in facilitating effective communication and coordination among team members. This interdisciplinary collaboration ensures that all aspects of the research, from study design to data analysis and interpretation, are conducted with precision and expertise.

Clinical-translational studies in diagnostics demand a comprehensive and multidisciplinary approach, where biostatisticians play a vital role in designing robust studies, analysing complex data, and providing valuable insights. Through these studies, diagnostic technologies can be validated, and their clinical relevance can be determined, ultimately leading to improved patient care and healthcare outcomes.

For more information on our services for clinical-translational studies see here.

23 Jul 2023

By Anatomise Biostats

0 Comments

Checklist for proactive regulatory compliance in medical device R&D projects

Clinical Trials, Med-Tech & Medical Device

In today’s competitive and highly regulated medical device industry, ensuring regulatory compliance is not merely a legal obligation—it is fundamental to guaranteeing the safety, efficacy, and overall quality of your innovations. Whether you are developing a new device or conducting clinical trials, a proactive and integrated approach to compliance can help you navigate the complexities of the regulatory landscape. This guide provides a comprehensive overview of strategies for embedding regulatory considerations into every stage of medical device R&D and clinical trial biostatistics, offering practical insights to build trust with regulatory authorities, healthcare professionals, and patients alike.

Medical Device R&D Regulatory Compliance

1. Early Involvement of Regulatory Experts
Bringing regulatory experts on board at the very start of your project can save you time, resources, and potential headaches later. Think of them as navigators who help chart the safest course through the regulatory landscape. They can provide insights on the necessary documentation, best practices to follow, and how to avoid pitfalls that might not be apparent until later stages. Their early input ensures that your R&D process is built on a strong foundation of compliance.

2. Stay Updated with Regulations
Regulations in the medical device arena are not static—they evolve as new technologies emerge and safety standards improve. Much like keeping up with the latest software updates for your smartphone, you need to remain informed so your processes remain secure and efficient. Regularly check for updates from agencies such as the MHRA or the European Medicines Agency, and consider subscribing to industry newsletters or alerts. This proactive approach enables you to adjust your strategy in real time, ensuring your project always meets the current standards.

3. Build a Strong Regulatory Team
A dedicated regulatory team acts as the backbone of your project. By assembling a group of professionals with expertise in regulatory affairs, quality assurance, and compliance, you create a supportive network that works closely with R&D, manufacturing, and quality control teams. This integrated approach ensures that everyone speaks the same language of compliance and that decisions are made with a clear understanding of the regulatory implications.

4. Conduct Regulatory Gap Analysis
Think of a gap analysis as a health check-up for your processes. It involves a thorough review of your current practices against the regulatory requirements. This analysis helps you identify areas needing improvement before they escalate into major issues. By addressing these gaps early, you can avoid costly delays or the need for rework later in the development cycle.

5. Implement Quality Management Systems (QMS)
A robust QMS is essential for managing the complexities of medical device development. Standards such as ISO 13485 provide a structured framework to ensure quality at every stage—from initial design through to post-market surveillance. Implementing a QMS means setting up systems for comprehensive documentation, process control, and continuous improvement, which not only aids in compliance but also enhances overall product quality.

6. Adopt Design Controls
Design controls are the guardrails of your R&D process. They ensure that every design decision is thoroughly documented, justified, and subject to review. This involves keeping detailed records of design changes, test results, and risk assessments. Following guidelines such as those from the MHRA or FDA helps create a transparent process that is easier to audit and defend during regulatory reviews.

7. Risk Management
In medical device development, risk management is an ongoing process. It involves identifying potential hazards, evaluating associated risks, and implementing strategies to mitigate these risks. Think of it as creating a safety net for your project—by planning for what might go wrong, you can reduce the impact of unforeseen issues and ensure that patient safety remains a top priority.

8. Clinical Trials and Data Collection
If your device requires clinical testing, planning and executing trials with regulatory compliance in mind is critical. This means designing studies that are ethically sound, statistically robust, and meticulously documented. Every detail—from the clinical trial protocol to ethics committee approvals—must align with regulatory expectations, ensuring that the data you collect is both credible and acceptable for submission.

9. Preparation for Regulatory Submissions
Preparing for regulatory submissions is like assembling all the pieces of a complex puzzle. Begin early by gathering and organising every document you will need, from technical files to test results. Early preparation provides ample time to review your submission package, ensuring that everything is accurate, complete, and compliant with the latest guidelines.

10. Engage with Regulatory Authorities
Maintaining open lines of communication with regulatory bodies can make a significant difference. By engaging with them throughout the development process—whether through pre-submission meetings or regular updates—you can clarify any uncertainties, receive valuable feedback, and build a positive rapport that may ease any challenges during the review process.

11. Post-Market Surveillance
Regulatory compliance does not end once your device hits the market. Post-market surveillance is essential to continuously monitor the device’s performance and safety. By systematically collecting and analysing data after commercialisation, you can promptly address any adverse events, update safety information, and ensure ongoing compliance with post-market regulatory requirements.

12. Training and Education
Continuous learning is key to maintaining a culture of compliance. Regular training sessions for everyone involved—from the R&D team to quality control—ensure that all members remain up to date on regulatory standards, understand how to implement them effectively, and recognise their roles in maintaining compliance. This not only minimises errors but also empowers your team to contribute actively to the project’s success.

Biostatistics Checklist for Regulatory Compliance in Clinical Trials

1. Early Biostatistical Involvement
Inviting biostatisticians to join the project at the very beginning helps shape the study design, data collection methods, and analysis plans right from the outset. Their expertise ensures that the study is methodologically sound and that statistical considerations are fully integrated into every stage of the trial.

2. Compliance with Regulatory Guidelines
Keeping abreast of guidelines such as ICH E9 and the MHRA’s or FDA’s guidance documents is crucial to ensure that your statistical methods meet regulatory expectations. This means routinely checking for updates and incorporating these standards into your analysis protocols, thereby building a strong case for the credibility and reliability of your findings.

3. Sample Size Calculation
Accurate sample size calculation is more than just a numerical exercise—it ensures that your study has enough power to detect clinically meaningful effects. This careful planning step guarantees that the trial is neither underpowered (risking inconclusive results) nor over-resourced (leading to unnecessary expense and complexity), thereby underscoring the scientific integrity of your study.

4. Randomisation and Blinding
Randomisation and blinding are fundamental practices for reducing bias in clinical trials. Implementing robust randomisation methods ensures that study groups are comparable, while effective blinding safeguards the integrity of the data by preventing any inadvertent influence on the study outcomes. These practices are vital for earning the trust of regulatory bodies and the scientific community.

5. Data Quality Assurance
Establishing comprehensive data quality assurance processes involves rigorous data monitoring, validation, and resolution of any queries. This step ensures that every data point is accurate and reliable, which is critical for drawing sound conclusions and making confident regulatory submissions.

6. Handling Missing Data
Missing data can undermine the validity of your analysis if not handled correctly. Developing clear strategies—whether through statistical imputation methods or sensitivity analyses—ensures that your conclusions remain robust and your study maintains its scientific rigour.

7. Adherence to the Statistical Analysis Plan (SAP)
The SAP serves as a blueprint for how the data will be analysed once collected. Adhering strictly to this plan ensures transparency and consistency in your analyses, supporting the integrity of your findings and providing a clear audit trail for regulators reviewing your work.

8. Statistical Analysis and Interpretation
Rigorous statistical analysis involves not only processing the numbers but also interpreting what they signify in the context of your study objectives. By carefully analysing and accurately interpreting the data, you can draw conclusions that are scientifically robust and align with regulatory expectations.

9. Interim Analysis (if applicable)
For some trials, interim analysis is conducted to assess the study’s progress and to inform necessary adjustments. When carried out according to the predefined protocol in the SAP, interim analysis can help ensure that the study remains on track and offers early insights to inform future decision-making—all while preserving the study’s integrity.

10. Data Transparency and Traceability
Maintaining detailed records and clear documentation of all data-related activities is essential. This practice ensures that every step of your analysis is transparent and that all data are easily traceable, which can prove invaluable during regulatory reviews and audits.

11. Regulatory Submissions
When compiling your submission package, the statistical sections of your Clinical Study Reports (CSRs) or Integrated Summaries of Safety and Efficacy must be thorough and well-organised. Clear, comprehensive statistical documentation helps regulators understand and trust your analysis methods and conclusions.

12. Data Security and Privacy
Protecting patient data is not only a regulatory requirement but also an ethical imperative. Implementing robust data security measures and adhering to data protection regulations (such as GDPR) ensures that all collected data are safeguarded, fostering trust among study participants and regulators alike.

13. Post-Market Data Analysis
Even after the clinical trial phase, ongoing data analysis is critical to monitor the long-term safety and effectiveness of the medical product. By planning for post-market data collection and analysis, you create a feedback loop that can inform future improvements and ensure sustained compliance with regulatory standards.

Achieving regulatory compliance in medical device development and clinical research is a multifaceted endeavour that demands continuous learning, proactive planning, and cross-disciplinary collaboration. By engaging experts early, staying current with evolving regulations, and implementing robust quality management, risk management, and statistical practices, organisations can not only streamline the approval process but also foster a culture of excellence and innovation. This comprehensive framework serves as a blueprint for ensuring that every step—from design and testing to market entry and post-market surveillance—is aligned with the highest standards of safety and efficacy, ultimately paving the way for long-term success and credibility in the healthcare industry.

Take advantage of a free initial consultation with Anatomise Biostats and plan the biometrics side of your product development early.

18 Jul 2023

By Anatomise Biostats

0 Comments

The Call for Responsible Regulations in Medical Device Innovation

Clinical Trials, Med-Tech & Medical Device

In the seemingly fast-paced world of medical technology, the quest for innovation is ever-present. However, it is crucial to recognise that the engineering of medical devices should not mirror the recklessness and hubris of exploratory engineering exemplified by the recent Ocean Gate tragedy where the stubborn blinkeredness of figures like Stockton Rush is not kept in check by sufficiently stringent regulations and safety standards. While it may seem in poor taste to criticise one who has lost their life under such tragic circumstances, the incident is absolutely emblematic of everything that can go wrong when the hubris of the innovator left relatively unbridled in the service of short-term commercial gains. More troubling in this case was that American safety standards were in place to protect human life, however the company was able to operate outside the United States jurisdiction in order to by-pass those standards. Fortunately, most medical device patients will not be receiving treatment over international waters. Despite this there exist loopholes to be filled.

The jurisdictional loophole of “export only” medical device approval

As of 2022 the United States pulls in 41.8% of global sales revenue from medical devices. 10% of Americans currently have a medical device implanted and 80,000 Americans have died as a result of medical devices over the past 10 years. Interestingly Americans have the 46^th highest life expectancy in the world despite having dis-proportionally high access to the most advanced medical treatments, including medical devices. Perhaps more worryingly, thousands of medical devices manufactured in the United States are FDA approved for “Export Only” meaning they do not pass the muster for use by American citizens. This “Export Only” status is one factor that partially accounts for America’s disproportionate share of the global medical device market. Foreign recipients of such medical devices are just as often from developed countries with their own high regulatory standards such as Australia, United Kingdom and Europe, and have accepted the device based on its stamp of approval by the FDA. Patients in these countries are typically not made aware of the particular risks, have not been disclosed the reasons why it has not been approved for use in the United States, nor that it has failed to gain this approval in its country of origin.

Local regulators such as the TGA in Australia, the MDR in Europe, or the MHRA in the UK, all claim to have some of the most stringent regulatory standards in the world. Despite this, American devices designated “Export Only” by the FDA, there are roughly 4600 in total, get approved predominantly due to differential device classification between the FDA and the importing country. By assigning a less risky class in the importing country the device escapes the need for clinical trials and the high level of regulatory scrutiny it was subject to in the United States. While devices that include medicines, tissues or cells are designated high risk in Australia and require thorough clinical validation, implantable devices for example can require only a CE mark by the TGA. This means that an implantable device such as a titanium shoulder replacement that has failed clinical studies in the United States and received an “Export Only” designation by the FDA can be approved by the TGA with or MDR with very little burden of evidence.

Regulatory standards must begin to evolve at the pace of technology.

Of equal concern is the need for regulatory standards that dynamically keep up with the pace of innovation and the emergent complexity of the devices we are now on a trajectory to engineer.

It is no longer enough to simply prioritise safety, regulation, and stringent quality control standards, we now need to have regular re-assessments of the standards themselves to evaluate whether they in-fact remain adequate to assess the novel case at hand. In many cases, even with current devices under validation, the answer to this question could well be “no”. It is quite possible that methods that would have previously seemed beyond consideration in the context of medical device evaluation, such as causal inference and agent-based models, may now become integrated into many a study protocol. Bayesian methods are also becoming increasingly important as a way of calibrating to increasing device complexity.

When the stakes involve devices implanted in people’s bodies or software making life-altering decisions, the need for responsible innovation becomes paramount.

If an implantable device also has a software component, the need for caution increases and exponentially so if the software is to be driven by AI. As these and other hybrid devices become the norm there is a need to test and thoroughly validate the reliability of machine learning or AI algorithms used in the device, the failure rate of software, and how this rate changes over time, software security and susceptibility to hacking or unintended influence from external stimuli, as well as the many metrics of safety and efficacy of the physical device itself.

The Perils of Recklessness:

Known for his audacious approach to deep-sea exploration, Stockton Rush has become a symbol of recklessness and disregard for safety protocols. While such daring may be thrilling in certain fields, it has no place in the medical device industry. Creating devices that directly impact human lives demands meticulous attention to detail, adherence to rigorous safety standards, and a focus on patient welfare.

There have been several class action lawsuits in recent years related to medical device misadventure. Behemoth Johnson & Johnson has been subject to several class action law suits pertaining to its medical devices. A recent lawsuit brought against the company, along with five other vaginal mesh manufacturers, was able to establish that 4000 adverse events had been reported to the FDA which included serious and permanent injury leading to loss of quality of life. Another recent class-action lawsuit relates to Johnson & Johnson surgical tools which are said to have caused at least burn injuries to at least 63 adults and children. These incidents are likely the result of recklessness in pushing these products to market and would have been avoidable had the companies involved chosen to conduct proper and thorough testing in both animals and humans. Proper testing occurs as much on the data side as in the lab and entails maintaining data integrity and statistical accuracy at all times.

Apple has recently been subject to legal action due to the their racially-biased blood oxygen sensor which, as with similar devices by other manufacturers, is able to detect blood oxygen more accurately for lighter skinned people than dark. Dark skin absorbs more light and can therefore give falsely elevated blood oxygen readings. It is being argued that users believing their blood oxygen levels to be higher than actual levels has contributed to higher incidences of death in this demographic, particularly during the pandemic. This lawsuit could have likely been avoided If the company had conducted more stringent clinical trials which recruited a broad spectrum of participants and stratified subjects by skin tone to fairly evaluate any differences in performance. If differences were identified, they should also have been transparently reported on the product label, if not also discussed openly in sales material, so that consumers can make an informed decision as to whether the watch was a good choice for them based on their own skin tone.

Ensuring Regulatory Oversight:

To prevent the emergence of a medtech catastrophes of unimagined proportions, robust regulation and vigilant oversight are crucial as we move into a newer technological era. Not just to redress current inadequacies in patient safeguarding but to also to prepare for new ones. While innovation and novel ideas drive progress, they must be tempered with accountability. Regulatory bodies play a vital role in enforcing safety guidelines, conducting thorough evaluations, and certifying the efficacy of medical devices before they reach the market. Striking the right balance between promoting innovation and safeguarding patient well-being is essential for the industry’s long-term success.

Any device given “Export Only” status by the FDA, or indeed by any other regulatory authority, should necessitate further regulatory testing in the jurisdictions in which it is intended to be sold and should by flagged by local regulatory agencies as insufficiently validated. Currently this seems to be taking place more in word than in deed under may jurisdictions.

Stringent Quality Control Standards:

The gravity of medical device development calls for stringent quality control standards. Every stage of the development process, from design and manufacturing to post-market surveillance, must prioritize safety, reliability, and effectiveness. Employing best practices, such as adherence to recognized international standards, robust testing protocols, and continuous monitoring, helps identify and address potential risks early on, ensuring patient safety remains paramount.

Putting Patients First:

Above all, the focus of medical device developers should always be on patients. These devices are designed to improve health outcomes, alleviate suffering, and save lives. A single flaw or an overlooked risk could have devastating consequences. Therefore, a culture that fosters a sense of responsibility towards patients is vital. Developers must empathize with the individuals who rely on these devices and remain dedicated to continuous improvement, addressing feedback, and learning from past mistakes.

Putting patient safety as the very top priority is the only way to avoid costly lawsuits and bad publicity stemming from a therapeutic device that was released onto the market too early in the pursuit of short-term financial gain. While product development and proper validation is an expensive and resource consuming process, cutting corners early on in the process will inevitably lead to ramifications at a later stage of the product life cycle.

Allowing overseas patients access to “export only” medical devices is attractive to their respective companies as it allows data to be collected from the international patients who use the device, which can later be used as further evidence of safety in subsequent applications to the FDA for full regulatory approval. This may not always be an acceptable risk profile for the patients who have the potential to be harmed. Another benefit of “Export Only” status to American device companies is that marketing the device overseas can bring in much needed revenue that enables further R&D tweaks and clinical evaluation that will eventually result in FDA approval domestically. Ultimately it is the responsibility of national regulatory agencies globally to maintain strict classification and clinical evidence standards lest their citizens become unwitting guinea pigs.

Collaboration and Transparency:

The medical device industry should embrace a culture of collaboration and transparency. Sharing knowledge, research, and lessons learned can help prevent the repetition of past mistakes. Open dialogue among developers, regulators, healthcare professionals, and patients ensures a holistic approach to device development, wherein diverse perspectives contribute to better, safer solutions. This collaborative mindset can serve as a safeguard against the emergence of reckless practices.

The risks associated with medical devices demand a paradigm shift within the industry. Developers must strive to distance themselves from the medtech version of Ocean Gate and instead embrace responsible innovation. Rigorous regulation, stringent quality control standards, and a relentless focus on patient safety should be the guiding principles of medical device development. By prioritising patient well-being and adopting a culture of transparency and collaboration, the industry can continue to advance while ensuring that every device that enters the market has been meticulously evaluated and designed with the utmost care.

Effective Strategies for Regulatory Compliance

1. Establish a Regulatory Compliance Plan: Develop a comprehensive plan that outlines the regulatory requirements and compliance strategies for each stage of the product development process.

2. Engage with Regulatory Authorities Early: Build relationships with regulatory authorities and engage with them early in the product development process to ensure that all requirements are met.

3. Conduct Risk Assessments: Identify potential risks and hazards associated with the product and develop risk management strategies to mitigate those risks.

4. Implement Quality Management Systems: Establish quality management systems that ensure compliance with regulatory requirements and promote continuous improvement.

5. Document Everything: Maintain detailed records of all activities related to the product development process, including design, testing, and manufacturing, to demonstrate compliance with regulatory requirements.

08 Jul 2023

By Anatomise Biostats

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Stata: Statistical Software for Regulatory Compliance in Clinical Trials

Biostatistics, Clinical Trials, Med-Tech & Medical Device, Pharma & Drug Development

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:

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:

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.

06 Jul 2023

By Anatomise Biostats

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

Biostatistics, Clinical Trials, Med-Tech & Medical Device, Pharma & Drug Development

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

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.

Dynamic Systems Modelling and Complex Adaptive Systems (CAS) Techniques in Biomedicine and Public Health

Uncategorized

Dynamical systems modelling is a mathematical approach to studying the behaviour of systems that change over time. These systems can be physical, biological, economic, or social in nature, and they are typically characterized by a set of variables that evolve according to certain rules or equations.

CAS (Complex Adaptive Systems) models are a specific type of dynamical systems model that are used to study systems that are complex, adaptive, and composed of many interconnected parts. These systems are often found in natural and social systems, and they are characterized by a high degree of uncertainty, nonlinearity, and emergence.

To build a dynamical systems model, one typically starts by identifying the variables that are relevant to the system being studied and the relationships between them. These relationships are usually represented by a set of equations or rules that describe how the variables change over time. The model is then simulated or analysed to understand the system’s behaviour under different conditions and to make predictions about its future evolution.

CAS models are often used to study systems that exhibit emergent behaviour, where the behaviour of the system as a whole is more than the sum of its parts. These models can help us understand how complex systems self-organize, adapt, and evolve over time, and they have applications in fields such as biology, economics, social science, and computer science.

Whatever the approach, a model is intended to represent the real system, but there are limits to the application of models. The reliability of any model often falls short when attempting to operate within and apply the parameter boundaries of the model to any real life context.

The previous article outlined some basic characteristics of complex adaptive systems (CAS). The CAS approach to modelling real world phenomena requires a different approach to the more conventional predictive modelling paradigm. Complex adaptive systems such as ecosystems, biological systems, or social systems require looking at interacting elements and observing the patterns that arise, creating boundary conditions from these patterns, running experiments or simulations, and responding to the outcomes in an adaptive way.

To further delineate the complex systems domain in practical terms we can use the Cynefin framework developed by David Snowden et al. to contrast the Simple, Complicated, Complex and Chaotic domains. For the purpose of this article the Chaotic domain will be ignored.

Enabling constraints of CAS models

In contrast to complex domain is the “known” or “simple” domain represented by ordered systems such as a surgical operating theatre or clinical trials framework. These ordered systems are rigidly constrained and can be planned and designed in advance based upon prior knowledge. In this context best practice can be applied because an optimal way of doing things is pre-determined.

The intermediary between the simple and complex domains is the “knowable” or ” complicated” domain. An example of such is the biostatistical analysis of basic clinical data. Within a complicated system there is a right answer that we can discover and design for. In this domain we can apply good practice based on expert advice (not best practice) as a right and wrong way of doing things can be determined with analysis.

Complex domain represents a system that is in flux and not predictable in the linear sense. A complex adaptive system can be operating in a state that is anywhere from equilibrium to the edge of chaos. In order to understand the system state one should perform experiments that probe relationships between entities. Due to the lack of linearity, multiple simultaneous experimental probes should occur in parallel, not in sequence, with the goal of better understanding processes. Emergent practice is determined in line with observed, evolving patterns. Ideally, decentralised Interpretation of data should be distributed to system users themselves rather than determined by a single expert in a centralised fashion.

As opposed to operating from a pre-supposed framework, the CAS structure should be allowed to emerge from the data under investigation. This avoids the confirmation bias that occurs when data are fitted to a predefined framework regardless of whether this framework best represents the data being modelled. Following on from this, model boundaries should also be allowed to emerge from the data itself.

Determining unarticulated needs from clusters of agent anecdotes or data points is a method of determining where improvement needs to occur in service provision systems. Yet this method forms an analogy that is mimicked in biological systems as well if an ABM was to be applied in a biomolecular context.

In understanding CAS, dispositionality of system states rather than linear causality should be the focus . Rather than presuming an inherent certainty as to “if I do A, B will result”, instead dispositional states arise as a result of A, which may result in B, but the evolution of which cannot be truly predicted.

“The longer you hold things in a state of transition the more options you’ve got” linear iterations based on a defined requirement with a degree of ambiguity which should be explored rather than eliminated. The opposite of standard statistical approach.

CAS modelling should include real-time feedback loops over multiple agents to avoid cognitive bias. In CAS modelling, every behaviour or interaction will produce unintended consequences, for this reason, David Snowden suggests, Small, fast experiments should be run in parallel, so that any bad, unintended consequences can be mitigated and the good ones amplified.

Modes of analysis and modelling:

System dynamics models (SDM)

A SDM simulates the movements of entities within the system and can be used to investigate macro behaviour of the system.
Changes to system state variables over time are modelled using differential equations.

SDMs are multi-dimentional, non-linear and include feedback mechanisms.

Visual representations of the model can be produced using stock and flow diagrams to summarise interdependencies between key system variables.

Dynamic hypotheses of the system model can be represented in a causal loop diagram
SDM is appropriate for modelling aggregate flows, trends, sub-system behaviour.

Agent based models (ABM)

ABMs can be used to investigate micro behaviour of the system from more of a bottom-up perspective through Intricate flows of individual based activity.
State changes of individual agents are simulated by ABMs rather than the broader entites captured by SDM
Multiple types of agent are operating within the same complex adaptive system modelled
Data within the ABM can be aggregated to infer more macro or top-down system behaviour.

Agents within the ABM can make decisions, engage in behaviour defined by simple rules and attributes, learn from experience and from feedback from interactions with other agents or the modelled environment. This is as true in models of human systems as it is with molecular scale systems. In both examples agents can par take in communication on a one to one, one to many and one to location basis. Previously popular models such as discrete event simulation (DES) was implemented to model passive agents at a finite time rather than active “decision makers” over dynamic periods that are a feature of ABMs.

Hybrid Models

Both ABM and SDM are complimentary techniques for simulating micro and macro level behaviour of complex adaptive systems and therefore engaging in exploratory style analysis of such systems.
Hybrid models emulate individual agent variability as well as variability in the the behaviour of aggregate entities they compose.
Simulate macro and micro level system behaviour in many areas of investigation such as health service provision, biomedical science.

Hybrid models have the ability to combine two or more types of simulation within the same model. These models can combine SDMs and ABMs, or other techniques, to address both top-down and bottom-up micro and macro dynamics in a single model that more closely captures whole system behaviour. This has the potential to elevate many of the necessary trade-offs of using one of the simulation types alone.

As software capability develops we are seeing an increased application of hybrid modelling techquiques. Previously wide-spread techniques such as DES and Markov models, which are one-dimentional, uni-directional, linear, are now proving inadequate in the task of modelling the complex adaptive and dynamic world we inhabit.

Model Validation Techniques

SDMs and ABMs are not fitted to observed data but instead use both qualitative and quantitative real world data to inform and develop the model and it’s parameters as a simulation of real world phenomena. For this reason model validation of SDMs and ABMs should be even more rigorous than for more traditional models such as maximum likelihood or least squares methods. Sensitivity analysis and validation tests such as behavioural validity tests can be used to compare model output against real-world data from organisations or experiments, relevant to the scale of the model being validated.

Structure of the model such as

Checking how the model behaves when subject to extreme parameter values.
Things like dimensional consistency, boundary adequacy, mass balance
Sensitivity analysis – how sensitive is the model to changes in key parameters.

Network Analysis

Data accrual from diverse data sources challenges and limitations

While complex systems theory has origins in the mathematics chaos theory, there are many examples contemporaneously where complex systems theory has been divorced form the mathematics and statistical modelling and applied in diverse fields such as business and healthcare or social services provision. Mathematical modelling adds validity to complex systems analysis. The problem with completing solely qualitative analysis without the empiricism of mathematical modelling, simulation and checking against a variety of real world data sets, the results

03 Sep 2019

By Anatomise Biostats

Latent Variable Modelling And The Chi Squared Exact Fit Statistic

Biostatistics

neural networks analysis latent variable models

chi squared exact fit statistic for model fitting in latent variable models

Latent variable modelling and the chi squared exact fit statistic

Latent variable models are exploratory statistical models used extensively throughout clinical and experimental research in medicine and the life sciences in general. Psychology and neuroscience are two key sub-disciplines where latent variable models are routinely employed to answer a myriad of research questions from the impact of personality traits on success metrics in the workplace (1) to measuring inter-correlated activity of neural populations in the human brain based on neuro-imaging data (2). Through latent variable modelling, dispositions, states or process which must be inferred rather than directly measured can be linked causally to more concrete measurements.
Latent variable models are exploratory or confirmatory in nature in the sense that they are designed to uncover causal relationships between observable or manifest variables and corresponding latent variables in an inter-correlated data set. They use structural equation modelling (SEM) and more specifically factor analysis techniques to determine these causal relationships which and allow the testing of numerous multivariate hypotheses simultaneously. A key assumption of SEM is that the model is fully correctly specified. The reason for this is this is that one small misspecification can affect all parameter estimations in the model, rendering inaccurate approximations which can combine in unpredictable ways (3).

With any postulated statistical model it is imperative to assess and validate the model fit before concluding in favour of the integrity of the model and interpreting results. The acceptable way to do this across all structural equation models is the chi squared (χ²) statistic.

A statistically significant χ² statistic is indicative of the following:

A systematically miss-specified model with the degree of misspecification a function of the χ² value.
The set of parameters specified in the model do not adequately fit the data and thus that the parameter estimates of the model are inaccurate. As χ² operates on the same statistical principles as the parameter estimation, it follows that in order to trust the parameter estimates of the model we must also trust the χ², or vice versa.

As a consequence there is a need for an investigation of where these misspecification have occurred and a potential readjustment of the model to improve its accuracy.

While one or more incorrect hypotheses may have caused the model misspecification, the misspecification could equally have resulted from other causes. It is important to thus investigate the causes of a significant model fit test . In order to properly do this the following should be evaluated:

Heterogeneity:
Does the causal model vary between sub groups of subjects?
Are there any intervening within subject variables?

Independence:
Are the observations truly independent?

Latent variable models involve two key assumptions: that all manifest variables are independent after controlling for any latent variables and, an individual’s position on a manifest variable is the result of that individual’s position on the corresponding latent variable (3).

Multivariate normality:
Is the multivariate normality assumption satisfied?

The study:

A 2015 meta-analysis of 75 latent variable studies drawn from 11 psychology journals has highlighted a tendency in clinical researchers to ignore the χ² exact fit statistic when reporting and interpreting the results of the statistical analysis of latent variable models (4).
97% of papers reported at least one appropriate model, despite the fact that 80% of these did not pass the criteria for model fit and the χ² exact fit statistic was ignored. Only 2% of overall studies concluded that the model doesn’t fit at all and one of these interpreted a model anyway (4).
Reasons for ignoring the model fit statistic: overly sensitive to sample size, penalises models when number of variables is high, general objection to the logic of exact fit hypothesis. Overall broach consensus of preference for Approximate fit indices (AFI).
AFI are instead applied in these papers to justify the models. This typically leads to questionable conclusions. In all just 41% of studies reported χ² model fit results. 40% of the studies that failed to report a p value for the reported χ² value did report a degrees of freedom. When this degrees of freedom was used to cross check the unreported p values, all non-reported p values were in fact significant.
The model fit function was usually generated through maximum likelihood methods, however 43% of studies failed to report which fit function was used.
A further tendency to accept the approximate fit hypothesis when in fact there was no or little evidence of approximate fit. This lack of thorough model examination empirical evidence of questionable validity. 30% of studies showed custom selection of more lax cut-off criteria for the approximate fit statistics than was conventionally acceptable, while 53% failed to report on cut-off criteria at all.
Assumption testing for univariate normality was assessed in only 24% of studies (4).
Further explanation of χ² and model fit:

The larger the data set the more that increasingly trivial discrepancies are detected as a source of model misspecification. This does not mean that trivial discrepancies become more important to the model fit calculation, it means that the level of certainty with which these discrepancies can be considered important has increased. In other words, the statistical power has increased. Model misspecification can be the result of both theoretically relevant and irrelevant/peripheral causal factors which both need to be equally addressed. A significant model fit statistic indicating model misspecification is not trivial just because the causes of the misspecification are trivial. It is instead the case that trivial causes are having a significant effect and thus there is a significant need for them to be addressed. The χ² model fit test is the most sensitive way to detect misspecification in latent variable models and should be adhered to above other methods even when sample size is high. In the structural equation modelling context of multiple hypotheses, a rejection of model fit does not result in the necessary rejection of each of the models hypotheses (4).
Problems with AFI:

The AFI statistic does provide a conceptually heterogeneous set of fit indices for each hypothesis, however none of these indices are accompanied by a critical value or significance level and all except one arise from unknown distributions. The fit indices are a function of χ² but unlike the χ² fit statistic they do not have a verified statistical basis nor do they present a statistically rigorous test of model fit. Despite this satisfactory AFI values across hypotheses are being used to justify the invalidity of a significant χ² test.
Mote Carlo simulations of AFI concluded that it is not possible to determine universal cut off criteria in any forms of model tested. Using AFI, the probability of correctly rejecting a mis-specified model decreased with increasing sample size. This is the inverse of the statistic. Another problem with AFI compared to χ² is that the more severe the model misspecification or correlated errors, the more unpredictable the AFI become. Again this is the inverse of what happens with the χ² statistic (4).
The take away:

Based on the meta-analysis the following best practice principles are recommended in addition to adequate attention to the statistical assumptions of heterogeneity, independence and multivariate normality outlined above:

Pay attention to distributional assumptions.
Have a theoretical justification for your model.
Avoid post hoc model modifications such as dropping indicators, allowing cross-loadings and correlated error terms.
Avoid confirmation bias.
Use an adequate estimation method.
Recognise the existence of equivalence models.
Justify causal inferences.
Use clear reporting that is not selective.

Image:

Michael Eid, Tanja Kutscher, Stability of Happiness, 2014 Chapter 13 – Statistical Models for Analyzing Stability and Change in Happiness
https://www.sciencedirect.com/science/article/pii/B9780124114784000138

References:
(1). Latent Variables in Psychology and the Social Sciences

(2) Structural equation modelling and its application to network analysis in functional brain imaging
https://onlinelibrary.wiley.com/doi/abs/10.1002/hbm.460020104

(3) Chapter 7: Assumptions in Structural Equation modelling
https://psycnet.apa.org/record/2012-16551-007

(4) A cautionary note on testing latent variable models
https://www.frontiersin.org/articles/10.3389/fpsyg.2015.01715/full

Author: Anatomise Biostats

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

The Data Manager’s Role in CRF Design

The Biostatistician’s Role in CRF Design

Why Both Roles Are Essential to MedTech Trials

1. Preventing Data Gaps

2. Ensuring Data Compatibility

3. Regulatory-Ready Analysis

Misconceptions About CRF Design in MedTech

The Risks of Excluding Biostatistician Input

Real-World Example: The Power of Collaboration

Practical Steps for MedTech Sponsors

Final Thoughts

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

1. Expertise Across Multiple Disciplines

2. Avoid Bottlenecks and Delays

3. Improved Data Quality Through Redundancy

4. Flexibility and Scalability

5. Ensuring Regulatory Compliance

6. Enhanced Data Security and Infrastructure

7. Hidden Challenges of Building an In-House Team

8. Unbiased Expertise

The Case for Outsourcing Biometrics

Synthetic Data:

3 key use cases for synthetic data in clinical research

Fake Data:

Medical Device R&D Regulatory Compliance

Biostatistics Checklist for Regulatory Compliance in Clinical Trials

The jurisdictional loophole of “export only” medical device approval

Regulatory standards must begin to evolve at the pace of technology.

The Perils of Recklessness:

Ensuring Regulatory Oversight:

Stringent Quality Control Standards:

Putting Patients First:

Collaboration and Transparency:

Effective Strategies for Regulatory Compliance

Stata for FDA regulatory compliance

Data management in Stata

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

Stata for clinical trials

Enabling constraints of CAS models

Modes of analysis and modelling:

System dynamics models (SDM)

Agent based models (ABM)

Hybrid Models

Model Validation Techniques

Network Analysis

Data accrual from diverse data sources challenges and limitations

Latent variable modelling and the chi squared exact fit statistic