I am completing my PhD at the STOR-i CDT in partnership with Roche. The project is focused on the design and analysis of basket trials. The following is a description of my PhD:<\/p>\n\n\n\n
Clinical trials are lengthy and costly procedures that investigate the efficacy and safety of treatments for human health conditions. Prior to their release onto the market, these treatments undergo rigorous testing. However, only a handful of the treatments that start testing are actually deemed safe and effective to treat the targeted condition. Also, patients’ responses to treatment vary based on their intrinsic factors and thus we wish to target treatments to the patients presenting different characteristics\/genetic aberrations, this is known as ‘personalized medicine’. <\/p>\n\n\n\n
Recent innovation in trial designs to improve study efficiency has led to the development of basket trials in which a single therapeutic treatment is tested on several patient populations, each of which form baskets. Patients across all baskets share a common genetic marker and as such, an assumption can be made that all patients will have a homogeneous response to treatments. Information borrowing procedures utilize this assumption to draw on information regarding the response in one basket when estimating the response rate in others. This can improve power of estimates particularly in the presence of small sample sizes. By using methods such as the Bayesian hierarchical model (BHM), Calibrated Bayesian hierarchical model (CBHM), Exchangeability-nonexchangeability (EXNEX) model and a Bayesian model averaging (BMA) procedure, notable improvements in power can be achieved. However, this can come at a large cost of inflated error rates, bringing into question validity of trial conclusions.<\/p>\n\n\n\n