Randomised control trials (RCTs) represent the mainstay of medicine, but are expensive, typically address narrowly defined populations and suffer from a long pathway to translation. This theme will focus on accelerating the pace and reducing the cost of clinical trials through the use of real world data, such as that collected to support clinical care, statistical methods for optimising trial design, workflow modelling for intelligent adaptive trials and use of computable phenotypes and natural language processing for cohort definition. This will involve the use and development of statistical methods for causal inference, and efficacy and mechanisms evaluation as well as the use of wearables for digital transformation of trial endpoints.
The partnerships with Janssen, Harvard, Cornell, and Michigan, among others, will help students develop skills to address issues such as the variability across data sets and drift across time in patient definitions.
