23 Jul 2023
By Anatomise Biostats
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Checklist for proactive regulatory compliance in medical device R&D projects

Clinical Trials, Med-Tech & Medical Device

Meeting regulatory compliance in medical device research and development (R&D) is crucial to ensure the safety, efficacy, and quality of the device. Here are some strategies to help achieve regulatory compliance:

  1. Early Involvement of Regulatory Experts: Engage regulatory experts early in the R&D process. Their insights can guide decision-making and help identify potential regulatory hurdles from the outset. This proactive approach allows for timely adjustments to the development plan to meet compliance requirements.
  2. Stay Updated with Regulations: Medical device regulations are continually evolving. Stay abreast of changes in relevant regulatory guidelines, standards, and requirements in the target markets. Regularly monitor updates from regulatory authorities to ensure that the R&D process aligns with the latest compliance expectations.
  3. Build a Strong Regulatory Team: Assemble a team of professionals with expertise in regulatory affairs and compliance. This team should collaborate closely with R&D, quality, and manufacturing teams to ensure that compliance considerations are integrated throughout the product development lifecycle.
  4. Conduct Regulatory Gap Analysis: Perform a comprehensive gap analysis to identify any discrepancies between current practices and regulatory requirements. Address the gaps proactively to avoid potential compliance issues later in the development process.
  5. Implement Quality Management Systems (QMS): Establish robust QMS compliant with relevant international standards, such as ISO 13485. The QMS should cover all aspects of medical device development, from design controls to risk management and post-market surveillance.
  6. Adopt Design Controls: Implement design controls, as per regulatory guidelines (e.g., FDA Design Controls). This ensures that the R&D process is well-documented, and design changes are carefully managed and validated.
  7. Risk Management: Conduct thorough risk assessments and establish a risk management process. Identify potential hazards, estimate risk levels, and implement risk mitigation strategies throughout the R&D process.
  8. Clinical Trials and Data Collection: If required, plan and conduct clinical trials to collect essential data on safety and performance. Ensure that clinical trial protocols comply with regulatory requirements, and obtain appropriate ethics committee approvals.
  9. Preparation for Regulatory Submissions: Early preparation for regulatory submissions, such as pre-submissions (pre-IDE or pre-CE marking) or marketing applications, is essential. Compile all necessary documentation, including technical files, to support regulatory approvals.
  10. Engage with Regulatory Authorities: Maintain open communication with regulatory authorities throughout the development process. Seek feedback, clarify uncertainties, and address any questions or concerns to facilitate a smoother regulatory review.
  11. Post-Market Surveillance: Plan post-market surveillance activities to monitor the device’s performance and safety after commercialisation. This ongoing data collection ensures compliance with post-market requirements and facilitates timely response to adverse events.
  12. Training and Education: Provide continuous training and education to the R&D team and other stakeholders on regulatory requirements and compliance expectations. This ensures that all members are aware of their responsibilities in maintaining regulatory compliance.

By implementing these strategies, medical device R&D teams can navigate the complex landscape of regulatory compliance more effectively. Compliance not only ensures successful product development but also builds trust with customers, stakeholders, and regulatory authorities, paving the way for successful market entry and long-term success in the medical device industry.

Biostatistics checklist for regulatory compliance in clinical trials

  1. Early Biostatistical Involvement: Engage biostatisticians from the outset to ensure proper study design, data collection, and statistical planning that align with regulatory requirements.
  2. Compliance with Regulatory Guidelines: Stay updated with relevant regulatory guidelines (e.g., ICH E9, FDA guidance) to ensure statistical methods and analyses comply with current standards.
  3. Sample Size Calculation: Perform accurate sample size calculations to ensure the study has sufficient statistical power to detect clinically meaningful effects.
  4. Randomisation and Blinding: Implement appropriate randomisation methods and blinding procedures to minimise bias and ensure the integrity of the study.
  5. Data Quality Assurance: Establish data quality assurance processes, including data monitoring, validation, and query resolution, to ensure data integrity.
  6. Handling Missing Data: Develop strategies for handling missing data in compliance with regulatory expectations to maintain the validity of the analysis.
  7. Adherence to SAP: Strictly adhere to the Statistical Analysis Plan (SAP) to maintain transparency and ensure consistency in the analysis.
  8. Statistical Analysis and Interpretation: Conduct rigorous statistical analyses and provide accurate interpretation of the results, aligning with the study objectives and regulatory requirements.
  9. Interim Analysis (if applicable): Implement interim analysis following the SAP, if required, to monitor study progress and make data-driven decisions.
  10. Data Transparency and Traceability: Ensure data transparency and traceability through clear documentation, well-organized datasets, and proper archiving practices.
  11. Regulatory Submissions: Provide statistical sections for regulatory submissions, such as Clinical Study Reports (CSRs) or Integrated Summaries of Safety and Efficacy, as per regulatory requirements.
  12. Data Security and Privacy: Implement measures to protect data security and privacy, complying with relevant data protection regulations.
  13. Post-Market Data Analysis: Plan for post-market data analysis to assess long-term safety and effectiveness, as required by regulatory authorities.

By following this checklist, biostatisticians can play a pivotal role in ensuring that clinical trial data meets regulatory approval and maintains data integrity, contributing to the overall success of the regulatory process for medical products.

18 Jul 2023
By Anatomise Biostats
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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 46th 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.

Further reading:

Law of the Sea and the Titan incident: The legal loophole for underwater vehicles – EJIL: Talk! (ejiltalk.org)

Drugs and Devices: Comparison of European and U.S. Approval Processes – ScienceDirect

https://www.theregreview.org/2021/10/27/salazar-addressing-medical-device-safety-crisis/

https://www.medtechdive.com/news/medtech-regulation-FDA-EU-MDR-2023-Outlook/641302/
https://www.marketdataforecast.com/market-reports/Medical-Devices-Market

FDA Permits ‘Export Only’ Medical Devices | Industrial Equipment News (ien.com)

FDA issues ‘most serious’ recall over Johnson & Johnson surgical tools (msn.com)

Jury Award in Vaginal Mesh Lawsuit Could Open Flood Gates | mddionline.com

Lawsuit alleges Apple Watch’s blood oxygen sensor ‘racially biased’; accuracy problems reported industry-wide – ABC News (inferse.com)

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
0 Comments

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:

  1. Datasignature Suite:

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

2. Variable labelling:

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

3. Value labels:

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

4. Data documentation:

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

5. Data transformation:

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

6. Data merging and appending:

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

7. Data export and import:

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

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

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

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

  1. CDISC Support:

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

2. Data Transformation:

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

3. Industry Adoption:

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

4. Learning Curve and Community Support:

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

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

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

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

06 Jul 2023
By Anatomise Biostats
0 Comments

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

  1. Industry Adoption:

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

2. Regulatory Compliance and CDISC standardisation:

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

3. Comprehensive Statistical Procedures:

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

4. Efficient Data Management:

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

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

5. Interactive and Reproducible Analysis:

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

6. Graphics and Visualization:

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

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

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

7. Active User Community and Support:

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

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

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

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

Further reading:

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