Statistical Methods for Biomarkers and Patient Reported Outcome in Cancer Trials

Led by: Joseph Ibrahim, PhD 

Randomized clinical trials are the primary mechanism by which new cancer therapies are tested for efficacy and evaluated for regulatory approval. The advent of novel biomarkers and emerging genomic technologies that may yield important new baseline predictors of primary clinical outcomes, the increasing emphasis on analyses of longitudinal progression of markers such as measures of quality of life, and the routine complications of missing information and subject drop-out present both challenges and opportunities for the interpretation of these studies. This project is focused on the development of new methodological advances to exploit prognostic auxiliary information and provide frameworks for analyses in the presence of missing data that will affect notably the strength and impact of inferences possible from current cancer clinical trials. 

This will be achieved through four aims: 

  1. Develop statistical methodology for determining the probability of success in cancer clinical trials. 
  2. Develop methodology for assessment of PROs and/or biomarkers in joint models of longitudinal and survival data. 
  3. Develop models and methods for multi-dimensional PROs and/or biomarkers and multivariate survival data. 
  4. Network meta-analysis for the identification of PROs and/or biomarkers.