Can the potential benefit of individualizing treatment be assessed using trial summary statistics alone?

TL;DR

This paper introduces a method to assess the potential benefits of personalized treatment using only summary statistics from clinical trials, helping determine if individualization is justified without detailed patient data.

Contribution

The authors develop bounds on treatment benefit using summary statistics, enabling evaluation of treatment individualization potential without individual-level data.

Findings

Method provides bounds on treatment benefit from summary data.

Applicable to stratified trial results with covariates.

Implemented in the rct2otrbounds R package.

Abstract

Individualizing treatment assignment can improve outcomes for diseases with patient-to-patient variability in comparative treatment effects. When a clinical trial demonstrates that some patients improve on treatment while others do not, it is tempting to assume that treatment effect heterogeneity exists. However, if variability in response is mainly driven by factors other than treatment, investigating the extent to which covariate data can predict differential treatment response is a potential waste of resources. Motivated by recent meta-analyses assessing the potential of individualizing treatment for major depressive disorder using only summary statistics, we provide a method that uses summary statistics widely available in published clinical trial results to bound the benefit of optimally assigning treatment to each patient. We also offer alternate bounds for settings in which trial results are stratified by another covariate. We demonstrate our approach using summary statistics from a depression treatment trial. Our methods are implemented in the rct2otrbounds R package, which is available at https://github.com/ngalanter/rct2otrbounds .