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Using pilot data to size a two-arm randomized trial to find a nearly optimal personalized treatment strategy.

Publication ,  Journal Article
Laber, EB; Zhao, Y-Q; Regh, T; Davidian, M; Tsiatis, A; Stanford, JB; Zeng, D; Song, R; Kosorok, MR
Published in: Stat Med
April 15, 2016

A personalized treatment strategy formalizes evidence-based treatment selection by mapping patient information to a recommended treatment. Personalized treatment strategies can produce better patient outcomes while reducing cost and treatment burden. Thus, among clinical and intervention scientists, there is a growing interest in conducting randomized clinical trials when one of the primary aims is estimation of a personalized treatment strategy. However, at present, there are no appropriate sample size formulae to assist in the design of such a trial. Furthermore, because the sampling distribution of the estimated outcome under an estimated optimal treatment strategy can be highly sensitive to small perturbations in the underlying generative model, sample size calculations based on standard (uncorrected) asymptotic approximations or computer simulations may not be reliable. We offer a simple and robust method for powering a single stage, two-armed randomized clinical trial when the primary aim is estimating the optimal single stage personalized treatment strategy. The proposed method is based on inverting a plugin projection confidence interval and is thereby regular and robust to small perturbations of the underlying generative model. The proposed method requires elicitation of two clinically meaningful parameters from clinical scientists and uses data from a small pilot study to estimate nuisance parameters, which are not easily elicited. The method performs well in simulated experiments and is illustrated using data from a pilot study of time to conception and fertility awareness.

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Published In

Stat Med

DOI

EISSN

1097-0258

Publication Date

April 15, 2016

Volume

35

Issue

8

Start / End Page

1245 / 1256

Location

England

Related Subject Headings

  • Statistics & Probability
  • Sample Size
  • Regression Analysis
  • Randomized Controlled Trials as Topic
  • Pregnancy
  • Precision Medicine
  • Pilot Projects
  • Models, Statistical
  • Male
  • Humans
 

Citation

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Laber, E. B., Zhao, Y.-Q., Regh, T., Davidian, M., Tsiatis, A., Stanford, J. B., … Kosorok, M. R. (2016). Using pilot data to size a two-arm randomized trial to find a nearly optimal personalized treatment strategy. Stat Med, 35(8), 1245–1256. https://doi.org/10.1002/sim.6783
Laber, Eric B., Ying-Qi Zhao, Todd Regh, Marie Davidian, Anastasios Tsiatis, Joseph B. Stanford, Donglin Zeng, Rui Song, and Michael R. Kosorok. “Using pilot data to size a two-arm randomized trial to find a nearly optimal personalized treatment strategy.Stat Med 35, no. 8 (April 15, 2016): 1245–56. https://doi.org/10.1002/sim.6783.
Laber EB, Zhao Y-Q, Regh T, Davidian M, Tsiatis A, Stanford JB, et al. Using pilot data to size a two-arm randomized trial to find a nearly optimal personalized treatment strategy. Stat Med. 2016 Apr 15;35(8):1245–56.
Laber, Eric B., et al. “Using pilot data to size a two-arm randomized trial to find a nearly optimal personalized treatment strategy.Stat Med, vol. 35, no. 8, Apr. 2016, pp. 1245–56. Pubmed, doi:10.1002/sim.6783.
Laber EB, Zhao Y-Q, Regh T, Davidian M, Tsiatis A, Stanford JB, Zeng D, Song R, Kosorok MR. Using pilot data to size a two-arm randomized trial to find a nearly optimal personalized treatment strategy. Stat Med. 2016 Apr 15;35(8):1245–1256.
Journal cover image

Published In

Stat Med

DOI

EISSN

1097-0258

Publication Date

April 15, 2016

Volume

35

Issue

8

Start / End Page

1245 / 1256

Location

England

Related Subject Headings

  • Statistics & Probability
  • Sample Size
  • Regression Analysis
  • Randomized Controlled Trials as Topic
  • Pregnancy
  • Precision Medicine
  • Pilot Projects
  • Models, Statistical
  • Male
  • Humans