CAPITAL: Optimal Subgroup Identification via Constrained Policy Tree
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- URL: http://arxiv.org/abs/2110.05636v1
- Date: Mon, 11 Oct 2021 22:41:07 GMT
- Title: CAPITAL: Optimal Subgroup Identification via Constrained Policy Tree
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- Authors: Hengrui Cai, Wenbin Lu, Rachel Marceau West, Devan V. Mehrotra, and
Lingkang Huang
- Abstract summary: A clinically meaningful subgroup learning approach should identify the maximum number of patients who can benefit from the better treatment.
We present an optimal subgroup selection rule (SSR) that maximizes the number of selected patients.
We propose a ConstrAined PolIcy Tree seArch aLgorithm to find the optimal SSR within the interpretable decision tree class.
- Score: 10.961093227672398
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Personalized medicine, a paradigm of medicine tailored to a patient's
characteristics, is an increasingly attractive field in health care. An
important goal of personalized medicine is to identify a subgroup of patients,
based on baseline covariates, that benefits more from the targeted treatment
than other comparative treatments. Most of the current subgroup identification
methods only focus on obtaining a subgroup with an enhanced treatment effect
without paying attention to subgroup size. Yet, a clinically meaningful
subgroup learning approach should identify the maximum number of patients who
can benefit from the better treatment. In this paper, we present an optimal
subgroup selection rule (SSR) that maximizes the number of selected patients,
and in the meantime, achieves the pre-specified clinically meaningful mean
outcome, such as the average treatment effect. We derive two equivalent
theoretical forms of the optimal SSR based on the contrast function that
describes the treatment-covariates interaction in the outcome. We further
propose a ConstrAined PolIcy Tree seArch aLgorithm (CAPITAL) to find the
optimal SSR within the interpretable decision tree class. The proposed method
is flexible to handle multiple constraints that penalize the inclusion of
patients with negative treatment effects, and to address time to event data
using the restricted mean survival time as the clinically interesting mean
outcome. Extensive simulations, comparison studies, and real data applications
are conducted to demonstrate the validity and utility of our method.
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