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Augmented two-stage estimation for treatment switching in oncology trials: Leveraging external data for improved
Harlan Campbell1,2, Nicholas Latimer3, Jeroen P Jansen1,4
1Precision AQ, HEOR - Evidence Synthesis and Decision Modeling, Vancouver, BC, Canada.
Statistical Methods in Medical Research
|September 30, 2025
Summary
Augmented two-stage estimation (ATSE) combines randomized trial data with external data to improve long-term treatment effect estimates. This new method shows promise for reducing bias and increasing precision when external data are unconfounded.
Area of Science:
- Biostatistics
- Clinical Trial Methodology
- Oncology Research
Background:
- Treatment switching in oncology randomized controlled trials (RCTs) complicates long-term effect estimation.
- Existing methods like two-stage estimation may yield imprecise results with high switching rates or limited sample sizes.
- External control arms using real-world data can be used but ignore valuable RCT data from non-switching participants.
Purpose of the Study:
- To introduce and evaluate a novel method, augmented two-stage estimation (ATSE), for more precise estimation of long-term treatment effects in oncology RCTs with treatment switching.
- To compare the performance of ATSE against traditional two-stage estimation and external control arm approaches using simulation studies.
Main Methods:
- Developed augmented two-stage estimation (ATSE) by creating a 'hybrid non-switching arm' combining data from RCT non-switchers and an external dataset.
- Assessed ATSE performance under specific assumptions: independence of switching from post-progression survival and exchangeability between RCT and external cohorts, conditional on covariates.
- Conducted simulation studies to compare ATSE with two-stage estimation and external control arm methods.
Main Results:
- Augmented two-stage estimation (ATSE) demonstrated potential for reduced bias and improved precision compared to two-stage estimation and external control arms when external data were unconfounded.
- The performance of ATSE was scenario-dependent.
- When external data were subject to unmeasured confounding, ATSE remained prone to bias, though generally to a lesser extent than the external control arm approach.
Conclusions:
- Augmented two-stage estimation (ATSE) offers a promising approach to enhance the accuracy of long-term treatment effect estimation in oncology RCTs with treatment switching.
- The method's effectiveness relies on the availability of unconfounded external data and the validity of its underlying assumptions.
- Further research is warranted to explore ATSE's robustness and applicability in diverse clinical trial settings.
Keywords:
Evidence synthesiscomparative effectivenesshealth technology assessmentsurvival analysistime-to-event outcomestreatment crossovertreatment switchingMore Related Videos
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