Genomic and clinical predictors for improving estimator precision in randomized trials of breast cancer treatments.

ABSTRACT:

Background

The hope that genomic biomarkers would dramatically and immediately improve care for common, complex diseases has been tempered by slow progress in their translation beyond bioinformatics. We propose a novel use of genomic information where the goal is to improve estimator precision in a randomized trial. We analyze the potential precision gains from the popular MammaPrint genomic signature and clinical variables in simulations of randomized trials analyzed using covariate adjustment.

Methods

We apply an estimator for the average treatment effect in the trial that adjusts for prognostic baseline variables to improve precision [1]. This precision gain can be translated directly into sample size reduction and corresponding cost savings. We conduct simulation studies

SUBMITTER: Patil P 

PROVIDER: S-EPMC5935844 | biostudies-literature | 2016 Aug

REPOSITORIES: biostudies-literature