The Progression‐Free‐Survival Ratio in Molecularly Aided Tumor Trials: A Critical Examination of Current Practice and Suggestions for Alternative Methods

The progression-free-survival ratio is a popular endpoint in oncology trials, which is frequently applied to evaluate the efficacy of molecularly targeted treatments in late-stage patients. Using elementary calculations and simulations, numerous shortcomings of the current methodology are pointed out. As a remedy to these shortcomings, an alternative methodology is proposed, using a marginal Cox model or a marginal accelerated failure time model for clustered time-to-event data. Using comprehensive simulations, it is shown that this methodology outperforms existing methods in settings where the intrapatient correlation is low to moderate. The performance of the model is further demonstrated in a real data example from a molecularly aided tumor trial. Sample size considerations are discussed.

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Publication Details

Journal
Biometrical Journal
Published
2024-12-18
DOI
https://doi.org/10.1002/bimj.70028
Citations
1
Primary Topic
Statistical Methods in Clinical Trials
Type
article
Field-Weighted Citation Impact
0.71
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article

The Progression‐Free‐Survival Ratio in Molecularly Aided Tumor Trials: A Critical Examination of Current Practice and Suggestions for Alternative Methods

Dominic Edelmann, Axel Benner, Peter Horak, Richard F. Schlenk et al.
1 citations
Biometrical Journal
Statistical Methods in Clinical Trials
0.71
article

The Progression‐Free‐Survival Ratio in Molecularly Aided Tumor Trials: A Critical Examination of Current Practice and Suggestions for Alternative Methods

Dominic Edelmann, Axel Benner, Peter Horak, Richard F. Schlenk, Tobias Terzer
article en
1 citations

Abstract

The progression-free-survival ratio is a popular endpoint in oncology trials, which is frequently applied to evaluate the efficacy of molecularly targeted treatments in late-stage patients. Using elementary calculations and simulations, numerous shortcomings of the current methodology are pointed out. As a remedy to these shortcomings, an alternative methodology is proposed, using a marginal Cox model or a marginal accelerated failure time model for clustered time-to-event data. Using comprehensive simulations, it is shown that this methodology outperforms existing methods in settings where the intrapatient correlation is low to moderate. The performance of the model is further demonstrated in a real data example from a molecularly aided tumor trial. Sample size considerations are discussed.

Biometrical JournalVol. 67(1)
German Cancer Research Center (DE), Heidelberg University (DE), University Hospital Heidelberg (DE), National Center for Tumor Diseases (DE)
Openalex Percentile: Top 23%
Statistical Methods in Clinical Trials
0.71
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