Efficient estimation of subgroup treatment effects using multi-source data

TL;DR

This paper introduces doubly robust, non-parametrically efficient methods for estimating subgroup treatment effects using multi-source data, accommodating flexible nuisance function estimation and applicable to various data integration scenarios.

Contribution

It develops novel estimators for subgroup effects from multi-source data, including methods for confidence interval construction and performance evaluation.

Findings

Estimators are non-parametrically efficient under mild conditions.

Methods perform well with large target population samples.

Application to schizophrenia trials demonstrates practical utility.

Abstract

Investigators often use multi-source data (e.g., multi-center trials, meta-analyses of randomized trials, pooled analyses of observational cohorts) to learn about the effects of interventions in subgroups of some well-defined target population. Such a target population can correspond to one of the data sources of the multi-source data or an external population in which the treatment and outcome information may not be available. We develop and evaluate methods for using multi-source data to estimate subgroup potential outcome means and treatment effects in a target population. We consider identifiability conditions and propose doubly robust estimators that, under mild conditions, are non-parametrically efficient and allow for nuisance functions to be estimated using flexible data-adaptive methods (e.g., machine learning techniques). We also show how to construct confidence intervals and simultaneous confidence bands for the estimated subgroup treatment effects. We examine the properties of the proposed estimators in simulation studies and compare performance against alternative estimators. We also conclude that our methods work well when the sample size of the target population is much larger than the sample size of the multi-source data. We illustrate the proposed methods in a meta-analysis of randomized trials for schizophrenia.