WS2: Safety Monitoring Statistical Methodology Workstream Focus: This workstream is focused on methods for quantitative analysis of safety data from various data sources, including clinical trials, postmarketing, and other healthcare data sources.
- Taskforce (TF-1): Benefit risk in multiregional clinical trial
- Who Are We: An ASA task force looking at quantitative methods applied to benefit risk analysis in multiregional clinical trial settings
- Purpose: aligned with ICH E17& E9, to provide guidance on benefit risk analysis in multiregional clinical trial settings.
- Background: Wrote book chapter on benefit risk
- Our Role: Review literature and case studies on benefit risk in multi-regional clinical trials to prepare and put together content for the 2022 Deming short course. In parallel, will try to draft a white paper (timing to be determined later)
- Taskforce (TF-2): Additional reference materials and tools for book chapters
- Who Are We: An ASA task force looking at quantitative methods applied to safety data in the drug's lifecycle
- Background: Wrote several book chapters on quantitative methods applied to safety
- Our Role: Develop training materials and identify/develop tools for implementing these methods outlined in the chapters
- Connection: We are looking for more participation and input on medical assessment, discernment, and interpretation of these methods in practice
- Taskforce (TF-3): Machine learning, artificial intelligence, and deep learning applied to drug safety data - methods, tools, and resources and use cases
- Who Are We: An ASA task force looking at ML, AI, and DL applied to drug safety data and use cases
- Background: With both clinical trials and healthcare in general evolving with regards to digital and big data regulatory agencies have been actively evaluating the impact of this ongoing development (Han et al (2017), Ly et al (2018), Gupta et al (2018), https://www.fda.gov/about-fda/website-policies/fda-social-media-policy, https://www.ema.europa.eu/en/news/artificial-intelligence-medicine-regulation, http://www.icmra.info/drupal/sites/default/files/2021-08/horizon_scanning_report_artificial_intelligence.pdf). It is quite evident that the use of ML, NLP, and DL in drug safety and pharmacovigilance will continue to experience a rapid uptake that will dictate a need to develop new regulations.
- Our Role: Review the literature and use case of machine learning, artificial intelligence, and deep learning applied to drug safety data. Draft a white paper (timing to be determined later) on the literature review findings
- Connection: We are looking for more participation and input on on both methodology and use of these methods and medical assessment, discernment, and safety interpretation of these methods in practice
- Taskforce (TF-4): Visual analytics methods, tools, and resources
- Who Are We: An ASA task force looking at approaches and use of visual analytics of drug safety and benefit risk data in particular those approaches that incorporate one or more quantitative elements
- Background: A continuation of the work from the book (Chapter 15) going beyond clinical trial data
- Our Role: Continue to identify tools, resources, and software for visual analytics of healthcare safety data and creation of training materials and use cases. Develop training materials and identify/develop and share tools for implementing the method and tools via some modality online or manuscript
- Connection: We are looking for more participation and input on medical understanding, assessment, discernment, and interpretation of these visual analytics and benefit risk methods in practice.
- Taskforce (TF-5): Digital health applied to drug safety
- Who Are We: An ASA task force looking at digital health in the context of drug safety data and methodology for analyzing data obtained using digital media for clinical trial and post-marketing data
- Background: Digital technologies continue to transform healthcare. Realizing the full potential of digital health can help accelerate the shift towards patient-centered and outcomes-focused access, sustainable healthcare. Regulatory agencies have also taken note of this development (see for example: https://www.fda.gov/medical-devices/digital-health-center-excellence)
- Our Role: Review the literature and use case of digital health and methods for analyzing data from digital tools
- Connection: We are looking for more participation and input on medical understanding, assessment, discernment, and interpretation of these visual analytics and benefit risk methods in practice