Bayesian approaches for survival data in pharmaceutical research.

In this research, we consider Bayesian methodologies to address problems in biopharmaceutical research, most of which are motivated by real-world problems in network meta-analysis, prior elicitation, and adaptive designs. Network meta-analysis is a hierarchical model used to combine the results of multiple studies, and allows for us to make direct and indirect comparisons between treatments. We investigate Bayesian network meta-analysis models for survival data based on modeling the log-hazard rates, as opposed to hazards ratios. Expert opinion is often needed to construct priors for time-to-event data, especially in pediatric and oncology studies. For this, we propose a prior elicitation method for the Weibull time-to-event distribution that is based on potentially observable time-to-event summaries which can be transformed to obtain a joint prior distribution for the Weibull parameters. Bayesian adaptive designs take advantage of accumulating information, by allowing key trial parameters to change in response to accruing information and predefined rules. We introduce a novel model-based Bayesian assessment of reading speed that uses an adaptive algorithm to target key reading metrics. These metrics are used in the assessment of reading speed in individuals with poor vision.