Double/debiased machine learning of quantile treatment effects on long-term outcomes in clinical trials

TL;DR

This paper develops a robust statistical method to estimate long-term treatment effects at different quantiles by combining randomized trial data with external observational data, addressing challenges of missing long-term outcomes.

Contribution

It introduces a doubly robust estimator that leverages machine learning for nuisance estimation, enabling accurate quantile treatment effect inference under data integration.

Findings

Estimator performs well in finite samples.

Method reveals heterogeneity across quantiles.

Handles flexible machine learning methods for nuisance functions.

Abstract

Long-term outcomes are often unavailable in randomized clinical trials, although short-term surrogate outcomes are commonly observed. External observational data may contain the long-term outcome, but causal comparisons based on such data alone are vulnerable to confounding. Existing surrogate-based data integration methods for long-term outcomes have focused primarily on average treatment effects. We study estimation of quantile treatment effects for long-term outcomes in the trial population by combining randomized trial data with external observational data. Under treatment randomization, positivity, and a surrogate-based transportability assumption, we establish identification and develop a doubly robust estimator for inference. The estimator accommodates flexible machine learning methods for nuisance estimation, remains consistent if either the score-related or outcome regression-related nuisance functions are consistently estimated, and is asymptotically normal under regularity conditions. Simulation and real-data results demonstrate that the proposed method performs well in finite samples and can reveal heterogeneous long-term treatment effects across quantiles.