Evaluating marker-guided treatment selection strategies.

Journal Article (Journal Article)

A potential venue to improve healthcare efficiency is to effectively tailor individualized treatment strategies by incorporating patient level predictor information such as environmental exposure, biological, and genetic marker measurements. Many useful statistical methods for deriving individualized treatment rules (ITR) have become available in recent years. Prior to adopting any ITR in clinical practice, it is crucial to evaluate its value in improving patient outcomes. Existing methods for quantifying such values mainly consider either a single marker or semi-parametric methods that are subject to bias under model misspecification. In this article, we consider a general setting with multiple markers and propose a two-step robust method to derive ITRs and evaluate their values. We also propose procedures for comparing different ITRs, which can be used to quantify the incremental value of new markers in improving treatment selection. While working models are used in step I to approximate optimal ITRs, we add a layer of calibration to guard against model misspecification and further assess the value of the ITR non-parametrically, which ensures the validity of the inference. To account for the sampling variability of the estimated rules and their corresponding values, we propose a resampling procedure to provide valid confidence intervals for the value functions as well as for the incremental value of new markers for treatment selection. Our proposals are examined through extensive simulation studies and illustrated with the data from a clinical trial that studies the effects of two drug combinations on HIV-1 infected patients.

Full Text

Duke Authors

Cited Authors

  • Matsouaka, RA; Li, J; Cai, T

Published Date

  • September 2014

Published In

Volume / Issue

  • 70 / 3

Start / End Page

  • 489 - 499

PubMed ID

  • 24779731

Pubmed Central ID

  • PMC4213325

Electronic International Standard Serial Number (EISSN)

  • 1541-0420

Digital Object Identifier (DOI)

  • 10.1111/biom.12179


  • eng

Conference Location

  • United States