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Selecting Optimal Subgroups for Treatment Using Many Covariates.

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.

SUBMITTER: VanderWeele TJ

PROVIDER: S-EPMC6456380 | biostudies-literature | 2019 May

REPOSITORIES: biostudies-literature

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Selecting Optimal Subgroups for Treatment Using Many Covariates.

VanderWeele Tyler J TJ Luedtke Alex R AR van der Laan Mark J MJ Kessler Ronald C RC

Epidemiology (Cambridge, Mass.) 20190501 3

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 op ...[more]

PMID: 30789432

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Project description:Rationale: No evidence-based tools exist to enhance precision in the selection of patient-specific optimal treatment durations to study in tuberculosis clinical trials. Objectives: To develop risk stratification tools that assign patients with tuberculosis into risk groups of unfavorable outcome and inform selection of optimal treatment duration for each patient strata to study in clinical trials. Methods: Publicly available data from four phase 3 trials, each evaluating treatment duration shortening from 6 to 4 months, were used to develop parametric time-to-event models that describe unfavorable outcomes. Regimen, baseline, and on-treatment characteristics were evaluated as predictors of outcomes. Exact regression coefficients of predictors were used to assign risk groups and predict optimal treatment durations. Measurements and Main Results: The parametric model had an area under the receiver operating characteristic curve of 0.72. A six-item risk score (HIV status, smear grade, sex, cavitary disease status, body mass index, and Month 2 culture status) successfully grouped participants into low (1,060/3,791; 28%), moderate (1,740/3,791; 46%), and high (991/3,791; 26%) risk, requiring treatment durations of 4, 6, and greater than 6 months, respectively, to reach a target cure rate of 93% when receiving standard-dose rifamycin-containing regimens. With current one-duration-fits-all approaches, high-risk groups have a 3.7-fold (95% confidence interval, 2.7-5.1) and 2.4-fold (1.9-2.9) higher hazard risk of unfavorable outcomes compared with low- and moderate-risk groups, respectively. Four-month regimens were noninferior to the standard 6-month regimen in the low-risk group. Conclusions: Our model discrimination was modest but consistent with current models of unfavorable outcomes. Our results showed that stratified medicine approaches are feasible and may achieve high cure rates in all patients with tuberculosis. An interactive risk stratification tool is provided to facilitate decision-making in the regimen development pathway.

Dataset Information

Selecting Optimal Subgroups for Treatment Using Many Covariates.

Publications

Selecting Optimal Subgroups for Treatment Using Many Covariates.

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