Case-only trees and random forests for exploring genotype-specific treatment effects in randomized clinical trials with dichotomous endpoints.

Abstract

Discovering gene-treatment interactions in clinical trials is of rising interest in the era of precision medicine. Nonparametric statistical learning methods such as trees and random forests are useful tools for building prediction rules. In this article, we introduce trees and random forests to the recently proposed case-only approach for discovering gene-treatment interactions and estimating marker-specific treatment effects for a dichotomous trial endpoints. The motivational example is a case-control genetic association study in the Prostate Cancer Prevention Trial (PCPT), which tested the hypothesis whether finasteride can prevent prostate cancer. We compare this novel approach to the interaction tree method previously proposed. Because of the modeling simplicity - directly targeting at interaction - and the statistical efficiency of the case-only approach, case-only trees and random forests yield more accurate prediction of heterogeneous treatment effects and better measure of variable importance, relative to the interaction tree method which uses data from both cases and controls. Application of the proposed case-only trees and random forests to the PCPT study yielded a discovery of genotypes that may influence the prevention effect of finasteride.