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Interpretable Dynamic Treatment Regimes.

Publication ,  Journal Article
Zhang, Y; Laber, EB; Davidian, M; Tsiatis, AA
Published in: J Am Stat Assoc
2018

Precision medicine is currently a topic of great interest in clinical and intervention science. A key component of precision medicine is that it is evidence-based, i.e., data-driven, and consequently there has been tremendous interest in estimation of precision medicine strategies using observational or randomized study data. One way to formalize precision medicine is through a treatment regime, which is a sequence of decision rules, one per stage of clinical intervention, that map up-to-date patient information to a recommended treatment. An optimal treatment regime is defined as maximizing the mean of some cumulative clinical outcome if applied to a population of interest. It is well-known that even under simple generative models an optimal treatment regime can be a highly nonlinear function of patient information. Consequently, a focal point of recent methodological research has been the development of flexible models for estimating optimal treatment regimes. However, in many settings, estimation of an optimal treatment regime is an exploratory analysis intended to generate new hypotheses for subsequent research and not to directly dictate treatment to new patients. In such settings, an estimated treatment regime that is interpretable in a domain context may be of greater value than an unintelligible treatment regime built using 'black-box' estimation methods. We propose an estimator of an optimal treatment regime composed of a sequence of decision rules, each expressible as a list of "if-then" statements that can be presented as either a paragraph or as a simple flowchart that is immediately interpretable to domain experts. The discreteness of these lists precludes smooth, i.e., gradient-based, methods of estimation and leads to non-standard asymptotics. Nevertheless, we provide a computationally efficient estimation algorithm, prove consistency of the proposed estimator, and derive rates of convergence. We illustrate the proposed methods using a series of simulation examples and application to data from a sequential clinical trial on bipolar disorder.

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Published In

J Am Stat Assoc

DOI

ISSN

0162-1459

Publication Date

2018

Volume

113

Issue

524

Start / End Page

1541 / 1549

Location

United States

Related Subject Headings

  • Statistics & Probability
  • 4905 Statistics
  • 3802 Econometrics
  • 1603 Demography
  • 1403 Econometrics
  • 0104 Statistics
 

Citation

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Zhang, Y., Laber, E. B., Davidian, M., & Tsiatis, A. A. (2018). Interpretable Dynamic Treatment Regimes. J Am Stat Assoc, 113(524), 1541–1549. https://doi.org/10.1080/01621459.2017.1345743
Zhang, Yichi, Eric B. Laber, Marie Davidian, and Anastasios A. Tsiatis. “Interpretable Dynamic Treatment Regimes.J Am Stat Assoc 113, no. 524 (2018): 1541–49. https://doi.org/10.1080/01621459.2017.1345743.
Zhang Y, Laber EB, Davidian M, Tsiatis AA. Interpretable Dynamic Treatment Regimes. J Am Stat Assoc. 2018;113(524):1541–9.
Zhang, Yichi, et al. “Interpretable Dynamic Treatment Regimes.J Am Stat Assoc, vol. 113, no. 524, 2018, pp. 1541–49. Pubmed, doi:10.1080/01621459.2017.1345743.
Zhang Y, Laber EB, Davidian M, Tsiatis AA. Interpretable Dynamic Treatment Regimes. J Am Stat Assoc. 2018;113(524):1541–1549.
Journal cover image

Published In

J Am Stat Assoc

DOI

ISSN

0162-1459

Publication Date

2018

Volume

113

Issue

524

Start / End Page

1541 / 1549

Location

United States

Related Subject Headings

  • Statistics & Probability
  • 4905 Statistics
  • 3802 Econometrics
  • 1603 Demography
  • 1403 Econometrics
  • 0104 Statistics