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Head to Head Comparisons Using Real World Data - Design and Datasource Considerations from Pilot Investigations in CVD

Comparisons of competing interventions are essential to determine value of medicines, both from clinical and societal perspectives. With head-to-head studies rare, HTA bodies rely on NMA techniques to derive the necessary estimates and incorporate them into cost-effectiveness models. The use of RWD for head-to-head comparison purposes was often challenged due to data limitations and difficulty to draw causal conclusions.

Recently, several research projects have already successfully replicated the results of clinical trials using RWD approaches and recently several life sciences companies supported their regulatory submissions with findings from RWD studies.

Following the well-attended introduction to this series, this second webinar, which will focus on using the approach of emulating target trials to develop head to head comparisons using RWD, continues our investigation of the topic of how to best design and execute these studies, depending on the research questions. This approach can be considered when:

  • Generating efficacy or safety evidence for conditional regulatory approval or post-market assessment
  • Providing a comparison when network meta analysis is not possible
  • Expanding the scope of a randomized trial
  • Refining aspects of an existing treatment protocol
  • Enabling a comparison to identifying optimal treatment regimes

The specific objectives of this webinar are to:

  • Briefly outline the concept of head to head comparison using RWD
  • Outline the design of the cardiovascular pilot investigation, including:
    • Research question(s) to be addressed
    • Specification of the target trial
    • Emulation of the target trial
  • Discuss the data requirements and the data source to be used in the pilot investigation with the focus on how to assess if data are sufficient for the purposes of trial emulation.

Meet the Speakers

Alind Gupta is statistician in the Real-world and Advanced Analytics team at Cytel focusing on machine learning and probabilistic modeling. His prior work has focused on graphical models in areas of heart disease, diabetes, chronic infectious disease and cancer for informing clinical risk prediction and health economic models. These have involved working with data from randomized trials, observational datasets from cancer registries and national health surveys, and unstructured biomedical text. Alind has a PhD from University of Toronto studying rare diseases.

Miguel Hernán conducts research to learn what works for the treatment and prevention of cancer, cardiovascular disease, and HIV infection. Together with his collaborators, he designs analyses of healthcare databases, epidemiologic studies, and randomized trials. Miguel teaches clinical data science at the Harvard Medical School, clinical epidemiology at the Harvard-MIT Division of Health Sciences and Technology, and causal inference methodology at the Harvard T.H. Chan School of Public Health, where he is the Kolokotrones Professor of Biostatistics and Epidemiology. His edX course Causal Diagrams and his book Causal Inference, co authored with James Robins, are freely available online and widely used for the training of researchers.

Judsen Schneider is an experienced technology leader with a background in Product Management, Data Science, Genetics, Machine Learning and Healthcare with a track record of success. Currently the Chief Technology Officer with Nashville Biosciences, Jud leads technology development, bioinformatics, and data science to help power clients' R&D efforts.

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