TITLE: Introduction to Quantitative Decision Making in Drug Development
SPEAKERS: Jerry Weaver, Teva and Joe Ibrahim, UNC
MODERATOR: Naitee Ting
Abstract:
Making decisions within drug development programs requires careful consideration of emerging data as well as leveraging established scientific beliefs such that the chances of success can be optimized while ensuring responsible stewardship of the research and development organization (i.e., how to best allocate limited capital). This gives rise to an area of statistical sciences which we refer to as Quantitative Decision Making (QDM). In essence, it can be conducted at various stages of drug development which impact study level decisions (such as interim analyses for stopping the trial for futility or making mid-stage design adaptations), project level decisions (such as interim analyses at the study level that results in launching another study thus accelerating the development program), or even portfolio level decisions (such as choosing or prioritizing a pipeline strategy with respect to optimizing net present value) by providing probabilities of success to help guide the decision making process. One critical aspect of implementing effective QDM is establishing clear decision criteria, aligned with the development strategy, prior to the incorporation of emerging data. Another aspect includes understanding and applying appropriate statistical methods which typically have a Bayesian element.
In this session the following topics will be presented:
- Target product profile and the QDM decision criteria framework applied at the project level
- Communication and presentation of go/ no go operating characteristics in QDM
- Conditional and unconditional probabilities as illustrated with power versus expected power (i.e., assurance)
- Bayesian philosophy and principles
- Constructing prior distributions either from study data or through the elicitation of expert opinion
- Implementation of Bayesian models
- Case examples involving various endpoint types (binary, continuous, counts, time-to-event)
- Monitoring a trial at multiple interims for futility
- Mid-trial design adaptations given interim results
- Development program acceleration given interim results
- Probability of pharmacological success at the end of phase 1 for dose selection in phase 2 and 3
- Probability of success in phase 3 for an in-licensing opportunity
Instructors’ Biography:
Jerry Weaver is currently the Global Statistics Therapeutic Head of Immunology and Head of Non-clinical Statistics at Teva. He has over 30 years of industry experience and has worked at well-established pharmaceutical companies that include Bristol-Myers Squibb, Celgene, Novartis, and Pfizer.
His leadership and experience in clinical development spans the therapeutic areas of immunology, fibrosis, oncology, neuroscience, cardiovascular, and anti-infectives; with medical affairs/ market access support in hematology/ oncology and immunology. As a leader in clinical statistics, he has provided statistical strategies on clinical development programs, helped design a multitude of phase 1-4 clinical trials, interacted with global regulatory agencies, participated in advisory committee meetings, supported labeling negotiations, and partnered with key opinion leaders in statistics. Jerry has a track record of six successful global submissions, three of which were under his oversight as a therapeutic area head at Celgene and Bristol-Myers Squibb. Jerry received his graduate degree in statistics from the University of Iowa in which studied under his advisor Robert V. Hogg. His interests include design of experiments, dose response modeling, quantitative go/ no go decision making, and Bayesian methods.
Dr. Joseph G. Ibrahim is an Alumni Distinguished Professor of Biostatistics at the University of North Carolina. Dr. Ibrahim’s areas of research focus are Bayesian inference, missing data, meta-analysis, network meta-analysis, Cancer research, and clinical trials. With over 37 years of experience working in Bayesian methods, Dr. Ibrahim directs the UNC Laboratory for
Innovative Clinical Trials. He is also the Director of Graduate Studies in UNC’s Department of Biostatistics. He is an Elected Fellow of ASA, IMS, ISBA, ISI, and RSS. He has published over 390 research papers, mostly in the top statistical journals. Dr. Ibrahim was awarded the 2024 Samuel S. Wilks Award. He has co-authored two advanced graduate-level books on Bayesian survival analysis and Monte Carlo methods in Bayesian computation and he is the PI of several grants from the NIH for his research on Bayesian methods, missing data, and cancer research.