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Epidemiology. 2019 May;30(3):334-341. doi: 10.1097/EDE.0000000000000991.

Selecting Optimal Subgroups for Treatment Using Many Covariates.

Author information

1
From the Departments of Epidemiology and Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA.
2
Fred Hutchinson Cancer Research Center, Seattle, WA.
3
Department of Biostatistics, University of California, Berkeley, CA.
4
Department of Health Care Policy, Harvard Medical School, Boston, MA.

Abstract

We consider the problem of selecting the optimal subgroup to treat when data on covariates are available from a randomized trial or observational study. We distinguish between four different settings including: (1) treatment selection when resources are constrained; (2) treatment selection when resources are not constrained; (3) treatment selection in the presence of side effects and costs; and (4) treatment selection to maximize effect heterogeneity. We show that, in each of these cases, the optimal treatment selection rule involves treating those for whom the predicted mean difference in outcomes comparing those with versus without treatment, conditional on covariates, exceeds a certain threshold. The threshold varies across these four scenarios, but the form of the optimal treatment selection rule does not. The results suggest a move away from the traditional subgroup analysis for personalized medicine. New randomized trial designs are proposed so as to implement and make use of optimal treatment selection rules in healthcare practice.

PMID:
30789432
PMCID:
PMC6456380
[Available on 2020-05-01]
DOI:
10.1097/EDE.0000000000000991

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