We propose a multi-metric flexible Bayesian framework to support efficient interim decision-making in multi-arm multi-stage phase II clinical trials.
Proposing and comparing methods of estimation for cluster-randomized test-negative design trials.
Note: This was originally the transcript of a 3-minute abstract presentation I gave during World TB Day 2023. I think it’s a nice, layman’s summary of some of the work I have been doing regarding decision-making.
De-risking decision-making for clinical trials in TB therapeutics How do YOU make decisions?
Let’s say your friends wanted to get tacos for lunch. [Bear with me for this analogy.] The most important step, without argument, is to identify a restaurant that has tacos on the menu.
My postdoctoral research at UCSF has focused on the development of a Bayesian-supported framework for decision-making in adaptive Phase II trials; work performed in collaboration with the UNITE4TB public-private consortium to advance Phase II clinical trial design and identify novel tuberculosis (TB) therapeutics. TB is the leading cause of death from an infectious disease worldwide. Though treatable and curable, current therapeutic regimens have been ineffective in controlling the spread of TB.