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A likely responder approach for the analysis of randomized controlled trials
Eugene Laska1, Carole Siegel1, Ziqiang Lin2
1Department of Psychiatry, New York University Grossman School of Medicine, One Park Avenue, New York, NY 10016, USA; Department of Population Health, Division of Biostatistics, NYU Grossman School of Medicine, 180 Madison Avenue, New York, NY 10016, USA.
Objective:
To further the precision medicine goal of tailoring medical treatment to individual patient characteristics by providing a method of analysis of the effect of test treatment, T, compared to a reference treatment, R, in participants in a RCT who are likely responders to T.
Methods:
Likely responders to T are individuals whose expected response at baseline exceeds a prespecified minimum. A prognostic score, the expected response predicted as a function of baseline covariates, is obtained at trial completion. It is a balancing score that can be used to match likely responders randomized to T with those randomized to R; the result is comparable treatment groups that have a common covariance distribution. Treatments are compared based on observed outcomes in this enriched sample. The approach is illustrated in a RCT comparing two treatments for opioid use disorder.
Results:
A standard statistical analysis of the opioid use disorder RCT found no treatment difference in the total sample. However, a subset of likely responders to T were identified and in this group, T was statistically superior to R.
Conclusion:
The causal treatment effect of T relative to R among likely responders may be more important than the effect in the whole target population. The prognostic score function provides quantitative information to support patient specific treatment decisions regarding T furthering the goal of precision medicine.
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