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
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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)

13 Dec 2022
By Sophia Puliasis

Sex Differences in Clinical Trial Recruiting

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

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

Sex difference by clinical trial phase

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

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

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

Reporting of safety and efficacy by sex difference

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

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

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

Sex differences by disease type and burden

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

Sex differences by disease type

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

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

Sex Differences by Disease Burden

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

Takeaways to improve your patient sample in clinical trial recruiting:

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

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

Conclusions

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

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

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

References:

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

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

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

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

01 Nov 2022
By Sarah Baker

Medical Device Categorisation, Classification and Regulation in the United Kingdom

Clinical Trials, Med-Tech & Medical Device

Contributor: Sana Shaikh

In this article

  • Overview of medical device categorisations and classifications for regulatory purposes in the United Kingdom
  • Summary of medical devices categorisations based on type, usage and risk potential during use as specified in the MDR and IVDR.
  • The class of medical device and its purpose determines the criteria required to meet regulatory approval. All medical devices in the UK must have a UKCA or CE marking depending on the legislation the device has been certified under.
  • Explanation of risk classifications for general medical devices and active implantable devices
  • Explanation of risk classifications for in vitro diagnostics

In the UK and EU medical devices are regulated under the Medical Devices Regulation (MDR) or In Vitro Diagnostics Regulation (IVDR) depending upon which category they fall under. In the UK it is the Medicines and Healthcare Products Regulatory Agency (MHRA) that is responsible for new product approval and market surveillance activities related to medical devices and other therapeutics, such as pharmaceuticals, intended for use in patients within the UK. The equivalent regulatory agency in the EU is the European Regulatory Agency (EMA). The MHRA also manages the Early Access to Medicines Scheme (EAMS) to enable patients access to pre-market therapeutics that are yet to receive regulatory approval where their medical needs are currently unmet by existing options. To qualify for EAMS a medicine must be designated as a Promising Innovative Medicine (PIM) based on early clinical data.

Having a thorough understanding of the classification and class of your medical device is vital for it to undergo the appropriate assessment route and be approved and ready for market. While the scope of medical devices is incredibly broad, for regulatory purposes they tend to be classified based on device type, duration of use and level of risk. Which risk class a device falls into will be determined in a large part by device type and duration of use, as both of these factors influence the level of risk to the patient. All medical devices in the UK must be designated a category and a risk classification in order to undertake the regulatory approval process.

Category (type) of Medical Device

The MHRA categorises medical devices into the following 5 categories:

  • Non-invasive – Devices which do not enter the body
  • Invasive – Devices which in whole or part are inserted into the body’s orifices (including the external eyeball surface) or through the surface or the body such as the skin.
  • Surgically invasive – Devices used or inserted surgically that penetrate the body through the surface of the body, such as through the skin.
  • Active – Devices requiring an external source of power, including stand-alone software.
  • Implantable – Devices intended to be totally or partially introduced into the human body (including to replace an epithelial surface or the surface of the eye) by surgical intervention and to remain in place for a period of time.

Duration of use category

Medical devices are then further categorised based upon their intended duration of use under normal circumstances.

  • Transient – intended for less than 60 minutes of continuous use.
  • Short term – intended for between 60 minutes to 30 days of continuous use.
  • Long term – intended for more than 30 days continuous use.

More information to aid accurate medical device categorisation in the UK and EU can be downloaded here: Medical devices: how to comply with the legal requirements in Great Britain – GOV.UK (www.gov.uk)

UKCA Mark & Conformity Assessment

Further to these use, duration and risk categories the HPRA designates 3 additional categories for the purposes of UKCA Mark and conformity assessment. These categories for the are:

  • General medical devices – most medical devices fall into this category.
  • Active implantable devices – devices powered by implants or partial implants intended to remain in the human body after a procedure.
  • In vitro diagnostics medical devices (IVDs) – equipment or system used in vitro to examine specimens from the human body.

UKCA mark and conformity assessment and subsequent labelling is a crucial procedure for a device to enter the UK market for use by patients. It should be noted that the UKCA mark is not recognised in the EU or Northern Ireland, who instead recognise the CE mark. Great Britain will not recognise the CE mark after 30 June 2023, thus it will be important to have both the UKCA and CE mark for widespread distribution of a medical device. These incompatibilities seem to have arisen largely as a result of Brexit.

Risk classification categories for general medical devices and active implantable devices

In The UK and EU there are 4 official risk-related classes for medical devices. These classes apply to both general medical devices and active implantable devices. As noted previously, the class a device falls into is largely informed by the category and the intended duration of use for the device.

  • Class I , which includes the subclasses Class Is (sterile no measuring function), Class Im (measuring function), and Class Ir (devices to be reprocessed or reused). Low risk of illness/injury resulting from use. Only self-assessment required to meet regulatory approval.
  • Class IIa Low to medium risk of illness/injury resulting from use. Notified Body approval required.
  • Class IIb Medium to high risk of illness/injury resulting from use. Notified Body approval required.
  • Class III high potential risk of illness/injury resulting from use. Notified Body approval required.

More details on these classes can be found below.

In Vitro Diagnostic Medical Devices (IVDs)

The IVDR categorise IVDs in to the following categories for the purpose of obtaining regulatory approval in Great Britain. IVDs do not harm patients directly in the same way that other medical devices can and are thus subject to different risk assessment.

  • General IVD medical devices
  • IVDs for self-testing – intended to be using by an individual at home.
  • IVDs stated in Part IV of the UK MDR 2002, Annex II List B
  • IVDs stated in Part IV of the UK MDR 2002, Annex II List A

A more detailed explanation of these categories can be found towards the end of this article.

The EU and Northern Ireland has moved away from this list style of classification and has recently implemented the following risk classes. There are 4 IVDR risk classes outlined in Annex VIII. It seems likely that Great Britain may follow this in future.

Risk Classes for IVDs

  • Class A – Laboratory devices, instruments and receptacles.
  • Class B – All devices not covered in the other classes.
  • Class C – High risk devices presenting a lower direct risk to the patient population. Includes diagnostic devices where failure to accurately diagnose could be life-threatening. Covers companion diagnostics, genetic screening and some self-testing.
  • Class D – Devices that pose a high direct risk to the patient population, and in some cases the wider population, relating to life threatening conditions, transmissible agents in blood, biological materials for transplantation in to the human body and other similar materials.

Risk categories for general medical devices and active implantable medical devices in detail

Class I devices

These are generally regarded as low risk devices and pose little risk of illness and injury. Such devices have minimal contact with patients and the lowest impact on patient health outcomes. To self-certify your product, you must confirm that it is a class I device1,3. This may involve carrying out clinical evaluations, notifying the Medicines and Healthcare products Regulatory Agency (MHRA) of proposals to perform clinical investigations, preparing technical documentation and drawing up a declaration of conformity1. In cases where the device includes sterile products or measuring functions, approval from a UK Approved Body may still be necessary3. Devices in this category include thermometers, stethoscopes, bandages and surgical masks.

Class IIa & IIb devices

Class IIa devices are generally regarded as medium risk devices and pose moderate risk of illness and injury. Both class IIa and IIb devices must be declared as such by applying to a UK Approved Body and performing a conformity assessment3, 4. For class IIa and IIb devices, there are several assessments. These include examining and testing the product or a homogenous batch of products, auditing the production quality assurance system, auditing the final inspection and testing or auditing the full quality assurance system3. include dental fillings, surgical clamps and tracheotomy tubes4 Class IIb devices include lung ventilators and bone fixation plates4.

Class III devices

These are considered high risk devices and pose substantial risk of illness and injury. Devices in this category are essential for sustaining human life and Due to the high-risk associated with class III devices, they are subject to the strictest regulations. In addition to the class IIa and IIb assessments, class III devices require a design dossier examination3. include pacemakers, ventilators, drug-coated stents and spinal disc cages.

Risk Categories for In Vitro Diagnostics in detail

These include but are not limited to reagents, instruments, software and systems intended for in vitro examination of specimens such as tissue donations and blood4. Most IVDs do not require intervention from a UK Approved Body5. However, for IVDs that are considered essential to health, involvement of a UK Approved Body is necessary5. The specific conformity assessment procedure depends on the category of IVD concerned5.

General IVDs

These are considered a low risk to patients and include clinical chemistry analysers, specimen receptacles and prepared selective culture media4. For general IVDs, involvement from a UK Approved Body is not required5. Instead, relevant provisions in the UK MDR 2002 must be met and self-declared prior to adding a UKCA mark to the device5,6.

IVDs for self-testing

These represent a low-to-medium risk to patients and include pregnancy self-testing, urine test strips and cholesterol self-testing4. In addition to conforming to requirements for general IVDs, applications for IVDs involved in self-testing must be sent to a UK Approved Body5. This enables examination of the design of the device, such as how suitable it is for non-professional users5.

IVDs stated in Part IV of the UK MDR 2002, Annex II List B

These represent medium-to-high risk to patients and include blood glucose self-testing, PSA screening and HLA typing4. Applications for devices in this category must be sent to a UK Approved Body5. This can enable auditing of technical documentation and the quality management system6.

IVDs stated in Part IV of the UK MDR 2002, Annex II List A.

These represent the highest risk to patients and include Hepatitis B blood-donor screening, ABO blood grouping and HIV blood diagnostic tests4. Due to the high risk associated with IVDs in this category, applications for devices in this category must be sent to a UK Approved Body5. By doing so, an audit of the quality management system can be performed as well as a design dossier review6. In addition, the UK Approved Body must verify each product or batch of products prior to being placed on the market5,6.

Proposed updates to medical device categories in the UK

Due to the quickly evolving state of medical technology, many items that did not previously count as a medical device, such as software and AI, are now needing to be considered as such. New proposals have been put forward as potential amendments to the existing regulations and risk classifications to accommodate newer technologies and devices. Among other proposed changes the following list of novel devices has been recommended for upgrade to the classification of highest risk Class III.

  • Active implantable medical devices and their accessories
  • in vitro fertilisation (IVF) and Assisted reproduction technologies (ART)
  • Surgical meshes
  • total or partial joint replacements
  • spinal disc replacements and other medical devices that come into contact with the spinal column
  • medical devices containing nano-materials
  • medical devices containing substances that will be introduced to the human body by one of various methods of absorption in order to achieve their intended function.
  • Active therapeutic devices with an integrated diagnostic function determining patient management such as closed loop or automated systems.

With the shift to a higher risk classification will come increased demand of clinical evidence and clinical testing, including clinical trials, in order for these devices to meet regulatory approval and reach the market. While an increased burden for the manufacturer this will be to the benefit patient safety and satisfaction for the end users. A full list of the proposed changes, including those outside of Class III, can be found here: Chapter 2: Classification – GOV.UK (www.gov.uk)

Medical devices are incredibly heterogenous, ranging from therapeutics and surgical tools to diagnostics and medical imaging software including machine learning and AI. Accordingly, medical device research and development often requires an interdisciplinary approach. During R&D, it is important to consider for whom the device is intended, how it will be used, and under what circumstances. Similarly, it is crucial to understand the risk status of the device. By considering these attributes, the device can be successfully assessed through the appropriate regulatory approval pathway.

References

Factsheet: medical devices overview – GOV.UK (www.gov.uk)

[1] https://www.gov.uk/government/collections/guidance-on-class-1-medical-devices

[2] https://www.gov.uk/guidance/medical-devices-how-to-comply-with-the-legal-requirements

[3] https://www.gov.uk/guidance/medical-devices-conformity-assessment-and-the-ukca-mark

[4] https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/640404/MDR_IVDR_guidance_Print_13.pdf[5] https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/946260/IVDD_legislation_guidance_-_PDF.pdf

[5] https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/946260/IVDD_legislation_guidance_-_PDF.pdf

[6] diagnostic medical devices IVD

31 Oct 2022
By James Des-Rosiers

Regulation of Connected Medical Devices and IOmT

Clinical Trials, Med-Tech & Medical Device
Collection and transmission of personal biologic and health information via IOmT connected medical devices requires regulatory oversight and has cybersecurity implications.

Connected medical devices (CMDs) can produce and transmitting patient data, allowing their condition to be monitored by healthcare professionals. They are often used in decentralised clinical trials (DCTs) outside of the clinical trial site, allowing for participants who wouldn’t usually be able to attend. CMDs have led to the Internet of Medical things, a connected network of systems and which produce, transmit and analyse patient data.

CMDs and IoMT have countless applications in the healthcare and medical technology (Medtech) industries, however these devices are susceptible to cyber-attacks and data leaks. These attacks include stealing and selling private patient data to third parties, denial of service (DOS) attacks, and altering medical data which can lead to improper diagnoses and treatments.

It has been suggested by multiple authors that CMDs and other wearable activity trackers are prone to cyber-attack is that data security and privacy issues are often not considered during their development (1). Regulations for the development of CMDs in the UK fall under two categories: regulations concerning medical devices in general, and regulations concerning IoMT including data protection and cybersecurity. Medtech companies must follow both types of regulations if they wish to sell CMDs in the UK and abroad. Here we discuss the current regulations for CMDs in the UK, how they may change in response to these security issues, and how this will impact clinical trials and the approval of CMDs.

Current Device Regulations

Regulations for medical devices in the UK need to be updated to better cover the risks associated with CMDs, as many of these devices can enter the UK market with little-to-no regulatory approval especially in terms of data security. Manufacturers currently need only a Conformité Européenne (CE) mark to be sold in the EU (1). With CE marking, devices are classified according to risk from lowest (Class I) to highest (Class III), with class I devices allowed to enter the market without prior data regarding their safety in the US, EU and Japan. Devices placed in class IIb or III must carry out an audit of the whole quality assurance system or undergo an “Annex III” examination which can include examination of each product/batch, audit of the final inspection, or an audit of the production quality assurance system (2). Clinical trials to evaluate the conformity of CMDs to medical device regulations will have at least one of the following aims: (a) to verify that under normal usage, the device achieves the performance intended by the manufacturer, (b) to establish its clinical benefit as specified by the manufacturer, and (c) to establish its clinical safety (3). Many wearable devices e.g. smartwatches and activity trackers can skip regulatory approval as they aren’t currently classed as CMDs, however to be utilised in DCTs, they will need to be approved as medical devices (4).

In the UK and EU, the General Data Protection Regulation (GDPR) covers the use of medical data, as well as the Data Protection Act 2018 (DPA) in the UK as of 1 January 2021 (5). These regulations prohibit the disclosure of private data to third parties without the patient’s consent and can only be used without consent in the case of direct care and healthcare quality improvement projects. On the 24th of November 2021, the UK government issued the Product Security and Telecommunications Infrastructure (PSTI) Bill to place increased cybersecurity standards on technology companies (6). Requirements of PSTI include banning default and weak passwords, investigation of compliance failures and being transparent about fixes to security issues, with hefty fines in place if these rules aren’t followed. These regulations will force Medtech companies to constantly update devices and software found to be at risk of cyber-attack, as well as keeping the public informed on the updates. In addition, NHS-contracted organisations need to follow the NHS Code of Confidentiality and Code of Practice (5). Medtech companies hoping to sell in the UK should ensure their device meets these NHS requirements, and the NHS Data Security and Protection Toolkit 2021 states that healthcare organisations must keep an inventory of CMDs in their network (7). While these regulations prevent CMD developers from directly releasing data to third parties, they will not prevent cyber-attacks.

On the 26th of June 2022, the UK Government had a press release in which they discussed future regulatory changes regarding CMDs and data security (8). As of the 30th of June 2023, CMDs will need to carry a UK Conformity Assessed (UKCA) marking to be sold in the UK instead of the current CE markings. The UKCA marking is not recognised by the EU market as it only complies to the UK Supply of Machinery (Safety) Regulations 2008 (9), meaning Medtech companies hoping to enter both markets will need to follow the regulations of both markings. In addition, the government intends to introduce pre-market regulations similar to the EU MDR General Safety and Performance Requirement (GSPR) 17.4 regarding cyber security for medical devices. Following this regulation, hardware, IT networks and security measures must meet minimum requirements including protection against unauthorised access needed to allow the software to run efficiently (10).

Potential future intersection between regulations for cybersecurity & medical devices.

Where regulation may fall short of innovation in the changing landscape and possible solutions

Currently, medical device regulations such as the Conformité Européenne (CE) and UKCA markings don’t intersect with cybersecurity and data protection regulations, meaning CMDs can currently be sold in the UK despite being susceptible to data leaks. There is no evidence to suggest that this will change soon, however possible future rules to combine these types of regulation may include classing data security as a component of patient safety in clinical trials. In addition, pre-market trials of CMD cybersecurity could be performed using simulated malware to test for vulnerabilities in CMDs, including software and AI networks (1). These regulations will force Medtech companies to consider the cybersecurity of their devices more strongly during the design and production stages of development, preventing cyber-attacks instead of retroactive changes following data leaks.

CMDs have revolutionised modern healthcare, however IoMT is still in its infancy and cybersecurity risks and subsequent regulatory changes are to be expected. These changes will likely stall the development and sale of CMDs due to increased care during development and stricter pre-market trials, however regulations are necessary to ensure patient data remains private for the safety and security of the public.

References:

1)     Hernández-Álvarez L, Bullón Pérez JJ, Batista FK, Queiruga-Dios A. Security Threats and Cryptographic Protocols for Medical Wearables. Mathematics. 2022 Mar 10;10(6):886. – Available from: https://doi.org/10.3390/math10060886

2)     CE Marking – Medical Devices Class III [Internet] 2021 – Available from: http://www.ce-marking.com/medical-devices-class-iii.html

3)     Reuschlaw – Need for clinical trials in accordance with the MDR [Internet] 2021 – Available from: https://www.reuschlaw.de/en/news/need-for-clinical-trials-in-accordance-with-the-mdr/

4)     Sato T, Ishimaru H, Takata T, Sasaki H, Shikano M. Application of Internet of Medical/Health Things to Decentralized Clinical Trials: Development Status and Regulatory Considerations. Frontiers in Medicine. 2022;9. doi: 10.3389/fmed.2022.903188

5)     TaylorWessing – Medical devices in the UK – the data protection angle [Internet] 2020 – Available from: https://globaldatahub.taylorwessing.com/article/medical-devices-in-the-uk-the-data-protection-angle

6)     Info Security Magazine – UK Introduces New Cybersecurity Legislation for IoT Devices [Internet] 2021 – Available from: https://www.infosecurity-magazine.com/news/uk-cybersecurity-legislation-iot/

7)     Core to Cloud – New mandatory cybersecurity requirements for medical devices [Internet] 2021 – Available from: https://www.coretocloud.co.uk/new-mandatory-cybersecurity-requirements-for-medical-devices/

8)     UK Government press release – UK to strengthen regulation of medical devices to protect patients [Internet] 2022 – Available from: https://www.gov.uk/government/news/uk-to-strengthen-regulation-of-medical-devices-to-protect-patients

9)     Make UK – CE Marking vs UKCA Marking – What does it mean? [Internet] 2020 – Available from: https://www.makeuk.org/insights/blogs/ce-marking-vs-ukca-marking

10)  EU Medical Device Regulation – ANNEX I – General safety and performance requirements [Internet] 2019 – Available from: https://www.medical-device-regulation.eu/2019/07/23/annex-i-general-safety-and-performance-requirements/

31 Oct 2022
By James Des-Rosiers

Cybersecurity Considerations for Connected Medical Devices and the “Internet of Medical Things”

Med-Tech & Medical Device
Cybersecurity for IOmT connected medical devices.

Advancements in technology of the past few decades has led to the development of devices capable of connecting to one another via networks such as Wi-Fi and Bluetooth, allowing them to create, transmit and receive data between one another. Medical technology (Medtech) companies have utilised these features to develop connected medical devices. These devices can transmit patient data such as heart rate, blood glucose levels and sleep patterns, which can be monitored by healthcare professionals and clinical trials companies, allowing for accurate remote oversight of a patient’s condition for quick and accurate diagnoses and treatment from anywhere.

The existence of connected medical devices has led to the Internet of Medical Things (IoMT), the connected network of health systems and services able to produce, transmit and analyse clinical data, which is changing the shape of healthcare and clinical trials globally.

Despite the clear potential of IoMTs in the healthcare system, there are several factors affecting the development of connected medical devices and their uptake by the public. Worries regarding the security of their private clinical data in the light of cybersecurity attacks over the past decade, and subsequent data protection regulations put in place to prevent further leaks and their potential impact on future innovations in the medtech industry.

Connected Medical Devices and the Internet of Medical Things (IoMT)

There are over 500,000 connected medical devices (CMDs) currently on the market (1), which can be split into three key groups; stationary medical devices typically found in hospitals such as CT and MRI scanners, implanted medical devices such as pacemakers and defibrillators to monitor a patient’s condition more closely, and wearable medical devices such as smartwatches that track patient activity and insulin pumps (1). Many technology companies, including those which wouldn’t be classified as Medtech (Apple, Nike, Huawei) produce smart devices which produce data surrounding user activity such as exercise, heart rate and quality of sleep. In November 2021, the FDA authorised the first prescription-use VR system for chronic lower back pain, further highlighting the increasing opportunities for CMDs in healthcare (2). Artificial intelligence (AI) and machine learning (ML) algorithms can also be classed under CMDs, capable of automated learning using neural networks to search and analyse data much faster (3). These AI are commonly used to search for novel patterns in data, diagnoses and predicting outcomes, and optimising patient treatments and are commonly used in clinical trials (3).

These devices, the data they produce and the development of software capable of compiling and analysing this data has led to the creation of the Internet of Medical Things (IoMT), which has the potential to revolutionise healthcare (1). IoMT allows healthcare professionals to monitor patients in real time from anywhere, increasing the speed and accuracy of diagnoses and treatment. General uptake of IoMT in healthcare may improve disease and drug management, leading to better patient outcomes and decreased costs to healthcare providers.

Medical Devices and Clinical Trials

CMDs have allowed for hybrid and decentralised clinical trials (DCTs), in which trials take place remotely from patient’s homes and during their daily lives instead of on a trial site. The prevalence of DCTs have increased significantly since the start of the COVID-19 pandemic, in which patient access to clinical trials was reduced by 80% and monthly trial starts decreased by 50% (4).

DCTs allow patients to take part who would usually be unable to participate due to geographical or time limitations, while reducing time spent on-site. According to a study by CISCRP, 60% of patients see the location and time spent in a clinical site as important factors when considering clinical trials (5). CMDs can include telemedicines, smart phone apps and AI capable of analysing patient data. As a result of this, there has been ~34% annual compound growth of CMD use in clinical trials (6).These benefits are best portrayed by the significant growth in the IoMT market, which is expected to grow from ~$31 billion in 2021 to a predicted ~$188 billion in 2028 (7), with CMDs and wearable smart devices increasingly used in the home as well as healthcare institutions.

Cybersecurity Issues

Despite the advantages of the IoMT, the adoption of CMDs is hampered by concerns regarding the security of clinical data stored in the cloud, instead of traditional medical records stored on paper or in internal servers which are less susceptible to being leaked. IoMT devices are vulnerable to many types of attack which can interfere with patient monitoring and care. Examples of these include eavesdropping, in which an attacker gains access to private medical records which can then be used to unlock the CMD, gaining further access to unauthorised data and allowing them to tamper with private medical records (8). While the common aim of these attacks is to sell this data to a third party, attacks on IoMT devices could include changing medical data leading to improper diagnoses of patients, the prescription of medication leading to an allergic response, and inaccurate monitoring of medical conditions which would impact patient welfare and have potentially significant financial impacts (8).

There have been many instances of attacks on large technology companies in recent years. Fitbit, one of the largest producers of wearable activity tracking watches, has been revealed to be vulnerable to data leakage via network connection (9), and the Nike+ Fuelband is prone to attack due to its USB connector (10). Technology companies such as Huawei, Xiaomi and Jawbone have suffered data leaks (9).

These incidents have negatively impacted public trust in CMDs collecting medical data, with people typically not wishing to share medical information with non-NHS businesses for reasons other than direct care. While trust was shown to increase after a deliberative workshop, it remained low (<50%) (11). As shown here, public distrust towards CMDs amid cybersecurity scandals will halt the potential growth of IoMT and its applications in healthcare.

CMDs and IoMT provide a promising avenue for quick, efficient diagnoses and treatment of a variety of conditions and allow for DCTs which increases the number of willing participants and allows for remote accurate monitoring of conditions. However, cybersecurity issues halt the progress and uptake of CMDs due to public distrust and misuse of the technology by cyber attackers. Unfortunately, cybersecurity issues can typically only be addressed after the incident occurs, however updates to UK regulations regarding CMDs will help prevent future attacks and data leaks.

Cybersecurity breaches can have a variety of goals.

1)     Deloitte – Medtech and the Internet of Medical Things [Internet] 2018 – Available from: https://www2.deloitte.com/global/en/pages/life-sciences-and-healthcare/articles/medtech-internet-of-medical-things.html

2)     Sato T, Ishimaru H, Takata T, Sasaki H, Shikano M. Application of Internet of Medical/Health Things to Decentralized Clinical Trials: Development Status and Regulatory Considerations. Frontiers in Medicine. 2022;9. – Available from: https://doi.org/10.3389%2Ffmed.2022.903188

3)     Angus DC. Randomized clinical trials of artificial intelligence. Jama. 2020 Mar 17;323(11):1043-5. – Available from: doi:10.1001/jama.2020.1039

4)     McKinsey & Company – No place like home? Stepping up the decentralization of clinical trials [Internet] 2021 – Available from: https://www.mckinsey.com/industries/life-sciences/our-insights/no-place-like-home-stepping-up-the-decentralization-of-clinical-trials

5)     Anderson A, Borfitz D, Getz K. Global public attitudes about clinical research and patient experiences with clinical trials. JAMA Network Open. 2018 Oct 5;1(6):e182969-. Available from: doi:10.1001/jamanetworkopen.2018.2969

6)     Marra C, Chen JL, Coravos A, Stern AD. Quantifying the use of connected digital products in clinical research. NPJ digital medicine. 2020 Apr 3;3(1):1-5. – Available from: https://doi.org/10.1038/s41746-020-0259-x

7)     Fortune Business Insights – Internet of Medical Things (IoMT) Market [Internet] – Available from: https://www.fortunebusinessinsights.com/industry-reports/internet-of-medical-things-iomt-market-101844

8)     Hasan MK, Ghazal TM, Saeed RA, Pandey B, Gohel H, Eshmawi AA, Abdel‐Khalek S, Alkhassawneh HM. A review on security threats, vulnerabilities, and counter measures of 5G enabled Internet‐of‐Medical‐Things. IET Communications. 2022 Mar;16(5):421-32. – Available from: https://doi.org/10.1049/cmu2.12301

9)     Jiang D, Shi G. Research on data security and privacy protection of wearable equipment in healthcare. Journal of Healthcare Engineering. 2021 Feb 5;2021. – Available from: https://doi.org/10.1155/2021/6656204

10)  Arias O, Wurm J, Hoang K, Jin Y. Privacy and security in internet of things and wearable devices. IEEE Transactions on Multi-Scale Computing Systems. 2015 Nov 6;1(2):99-109. DOI: 10.1109/TMSCS.2015.2498605

11)  Chico V, Hunn A, Taylor M. Public views on sharing anonymised patient-level data where there is a mixed public and private benefit. NHS Health Research Authority, University of Sheffield School of Law. 2019 Sep. – Available from: https://s3.eu-west-2.amazonaws.com/www.hra.nhs.uk/media/documents/Sharing_anonymised_patient-level_data_where_there_is_a_mixed_public_and_privat_Pab71UW.pdf

12 Mar 2022
By Sabine Dreibe

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

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

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

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

Classification criteria for medical devices

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

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

Food and Drug Administration (FDA)

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

Key stages of medical device clinical trials

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

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

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

Clinical evaluation for medical devices

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

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

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

The importance of evaluation

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

Post-market surveillance

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

The use of databases in medical device clinical trials

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

Bayesian methods as an alternative framework for evaluation

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

Use of placebo in medical vs pharmaceutical trials

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

Conclusion

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

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

References:

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

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

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

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

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

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