Statistical Methods for Analyzing DNA Methylation Data and Subpopulation Analysis of Continuous, Binary and Count Data for Clinical Trials

摘要: Medical research is increasingly focused on personalizing the care of patients. A better understanding of the interaction between treatment and patient specific prognostic factors will enable practitioners to expand the availability of tailored therapies improving patient outcomes. The Subpopulation Treatment Effect Pattern Plot (STEPP) approach was developed to allow researchers to investigate the heterogeneity of treatment effects on survival outcomes across increasing values of a continuously measured covariate, such as biomarker measurement. I extend the STEPP approach to continuous, binary and count outcomes which can be easily modeled with generalized linear models (GLM). The statistical significance of any observed heterogeneity of treatment effect is assessed using permutation tests. The method is implemented in the R software package (stepp) and is available in R version 3.1.1. The efficacy of my STEPP extension is demonstrated by using simulation and data application.