Optimal two-stage dynamic treatment regimes from a classification perspective with censored survival data.

Published

Journal Article

Clinicians often make multiple treatment decisions at key points over the course of a patient's disease. A dynamic treatment regime is a sequence of decision rules, each mapping a patient's observed history to the set of available, feasible treatment options at each decision point, and thus formalizes this process. An optimal regime is one leading to the most beneficial outcome on average if used to select treatment for the patient population. We propose a method for estimation of an optimal regime involving two decision points when the outcome of interest is a censored survival time, which is based on maximizing a locally efficient, doubly robust, augmented inverse probability weighted estimator for average outcome over a class of regimes. By casting this optimization as a classification problem, we exploit well-studied classification techniques such as support vector machines to characterize the class of regimes and facilitate implementation via a backward iterative algorithm. Simulation studies of performance and application of the method to data from a sequential, multiple assignment randomized clinical trial in acute leukemia are presented.

Full Text

Duke Authors

Cited Authors

  • Hager, R; Tsiatis, AA; Davidian, M

Published Date

  • December 2018

Published In

Volume / Issue

  • 74 / 4

Start / End Page

  • 1180 - 1192

PubMed ID

  • 29775203

Pubmed Central ID

  • 29775203

Electronic International Standard Serial Number (EISSN)

  • 1541-0420

International Standard Serial Number (ISSN)

  • 0006-341X

Digital Object Identifier (DOI)

  • 10.1111/biom.12894

Language

  • eng