Head to Head Comparisons Using Real World Data
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.
The increased availability of regulatory-grade RWD is challenging this status quo. The biases that used to plague the use of observational data can now be avoided. Specifically, we can eliminate immortal time bias and selection bias due to prevalent users. Although other challenges of working with RWD (e.g., confounding) continue, the approach of emulating target trials makes causal inference and head-to-head comparative effectiveness using RWD a reality.
Examples of the use of the approach include:
- Generation of efficacy or safety evidence for conditional regulatory approval or post-market assessment
- Refining aspects of an existing treatment protocol
- Providing a comparison when network meta analysis is not possible
- Expanding the scope of a randomized trial
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.
In this webinar series you'll hear from our team of experts as they embark on two pilot projects on head-to-head comparisons using real world data. These projects in oncology and cardiovascular will occur in real time across this webinar series. You will see the data and analytical challenges faced during the projects, and how these challenges are overcome. The objectives of this first webinar in the series are to:
- Introduce the concept of head to head comparison using RWD
- Present case studies of the approach
- Outline the plan of pilot investigations in oncology and cardiovascular disease
Miguel Hernán, MD, DrPH, Professor, Harvard University
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.
Devon Boyne, PhD Candidate, Director of Epidemiology, Cytel
Devon Boyne is a Director of Epidemiology at Cytel. He is a PhD Candidate in Epidemiology in the Department of Community Health Sciences at the University of Calgary and holds an M.Sc. in Epidemiology from Queen’s University and a Certificate in Data Analysis from the SAS Institute. His research interests include the emulation of target trials using real-world evidence, the development and assessment of clinical prediction models, and methods for conducting indirect treatment comparisons and network meta-analysis. Outside of his research, Devon has helped to teach numerous graduate-level courses in epidemiology and statistics and continues to attend workshops in advanced analytic techniques such as g-methods, multistate models, mixture models, and quantitative bias analysis.