# Integrative analysis of high-dimensional RCT and RWD subject to censoring and hidden confounding

## TL;DR

This study combines clinical trial and real-world data to better estimate how different treatments affect survival outcomes in lung cancer patients.

## Contribution

A new penalized-regression-based method is proposed to estimate heterogeneous treatment effects while accounting for unmeasured confounding.

## Key findings

- The integrative approach improves the efficiency of heterogeneous treatment effect estimation.
- Unmeasured confounding was detected in the analysis of lung cancer treatment outcomes.
- The method was applied to a real-world dataset and a randomized trial on lung cancer resection.

## Abstract

In this study, we focus on estimating the heterogeneous treatment effect (HTE) for survival outcome. The outcome is subject to censoring and the number of covariates is high-dimensional. We utilize data from both the randomized controlled trial (RCT), considered as the gold standard, and real-world data (RWD), possibly affected by hidden confounding factors. To achieve a more efficient HTE estimate, such integrative analysis requires great insight into the data generation mechanism, particularly the accurate characterization of unmeasured confounding effects/bias. With this aim, we propose a penalized-regression-based integrative approach that allows for the simultaneous estimation of parameters, selection of variables, and identification of the existence of unmeasured confounding effects. The consistency, asymptotic normality, and efficiency gains are rigorously established for the proposed estimate. Finally, we apply the proposed method to estimate the HTE of lobar/sublobar resection on the survival of lung cancer patients. The RCT is a multicenter non-inferiority randomized phase 3 trial, and the RWD comes from a clinical oncology cancer registry in the United States. The analysis reveals that the unmeasured confounding exists and the integrative approach does enhance the efficiency for the HTE estimation.

The online version contains supplementary material available at 10.1007/s10985-025-09654-1.