Design considerations and analytical framework for reliably identifying a beneficial individualized treatment rule

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Abstract

An individualized treatment rule (ITR) formalizes personalized medicine by assigning treatment as a function of patients’ clinical information, which contrasts with a static treatment rule that assigns everyone the same treatment. ITR identification has become a common aim in randomized clinical trials but sample size considerations for this aim are lacking. One approach is to select a sample size that will reliably identify an ITR with a performance close to the theoretical optimal rule. However, this approach could still lead to identifying ITRs that perform worse than the optimal static rule, particularly in the absence of substantial effect heterogeneity. This limitation motivates sample size considerations aimed at reliable identification of a beneficial ITR, which outperforms the optimal static rule, and analysis methods that identify the estimated optimal static rule when there is substantial uncertainty about whether an ITR will improve outcomes. To address these limitations, we propose a sample size approach based on the probability of identifying a beneficial ITR and introduce an approach for selecting the LASSO penalty parameter such that in the absence of treatment effect heterogeneity the estimated optimal static rule is identified with high probability. We apply these approaches to the PLUTO trial aimed at developing methods to assist with smoking cessation.

Original languageEnglish (US)
Article number106951
JournalContemporary Clinical Trials
Volume123
DOIs
StatePublished - Dec 2022

Bibliographical note

Funding Information:
This work was partially supported by NIH/NCI awards R01CA196873 , R01CA225190 , R01CA214825 , P30-CA0077598 , NIH/NIDA award R01DA046320 , NIH/NCATS award UL1TR002494 . TM would also like to acknowledge Medtronic Inc. for support in the form of a Faculty Fellowship.

Publisher Copyright:
© 2022 Elsevier Inc.

Keywords

  • Clinical trials
  • Individualized treatment rule
  • LASSO
  • Personalized medicine

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