Augmented two-stage estimation for treatment crossover in oncology trials: Leveraging external data for improved precision

TL;DR

This paper proposes an augmented two-stage estimation method that combines trial data with external data to improve treatment effect estimates in oncology trials with crossover, showing benefits in bias reduction and precision under certain conditions.

Contribution

The paper introduces augmented two-stage estimation (ATSE), a novel method that leverages external data alongside non-crossover trial participants for better treatment effect estimation.

Findings

ATSE reduces bias when external data are unconfounded.

ATSE improves precision over traditional methods.

Performance depends on scenario characteristics and data quality.

Abstract

Randomized controlled trials (RCTs) in oncology often allow control group participants to crossover to experimental treatments, a practice that, while often ethically necessary, complicates the accurate estimation of long-term treatment effects. When crossover rates are high or sample sizes are limited, commonly used methods for crossover adjustment (such as the rank-preserving structural failure time model, inverse probability of censoring weights, and two-stage estimation (TSE)) may produce imprecise estimates. Real-world data (RWD) can be used to develop an external control arm for the RCT, although this approach ignores evidence from trial subjects who did not crossover and ignores evidence from the data obtained prior to crossover for those subjects who did. This paper introduces ''augmented two-stage estimation'' (ATSE), a method that combines data from non-switching participants in a RCT with an external dataset, forming a ''hybrid non-switching arm''. With a simulation study, we evaluate the ATSE method's performance compared to TSE crossover adjustment and an external control arm approach. Results indicate that performance is dependent on scenario characteristics, but when unconfounded external data are available, ATSE may result in less bias and improved precision compared to TSE and external control arm approaches. When external data are affected by unmeasured confounding, ATSE becomes prone to bias, but to a lesser extent compared to an external control arm approach.