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

Biometrics. 2018 Dec;74(4):1180-1192. doi: 10.1111/biom.12894. Epub 2018 May 18.


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.

Keywords: Doubly robust; Inverse probability weighting; Optimal treatment regime; Sequential multiple assignment randomized trial; Support vector machine; Value search.

Publication types

  • Research Support, N.I.H., Extramural

MeSH terms

  • Acute Disease
  • Algorithms
  • Biometry / methods*
  • Computer Simulation
  • Decision Support Techniques*
  • Humans
  • Leukemia
  • Outcome Assessment, Health Care / methods*
  • Outcome Assessment, Health Care / standards
  • Randomized Controlled Trials as Topic
  • Support Vector Machine*
  • Survival Analysis*