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<p>Clinical researchers frequently need to answer: 'If a treatment changes endpoint 1 by X%, what change in endpoint 2 should we expect?' Current meta-analytic approaches either produce biased estimates (Daniels-Hughes) or provide correlation measures without explicit translational coefficients. We demonstrate how multivariate random-effects meta-analysis can derive the Expected Translational Association (η), which translates treatment effects across correlated endpoints, simultaneously fitting all correlated endpoints through their shared variance–covariance structure, yielding unbiased estimates and nominal confidence coverage. For convenience, we refer to this stacked implementation as SLIM (Stacked LInear Mixed Effects Model). Extensive simulations demonstrated that SLIM maintains near-zero bias and nominal coverage across scenarios, while the Daniels-Hughes approach exhibits substantial bias due to measurement-error-in-covariates, with bias persisting as sample size increases. Using the abdominal aortic aneurysm (AAA) dataset, we demonstrate how this approach quantifies translational associations between growth-rate and rupture-risk endpoints and enables quantitative biomarker–outcome "what if" analyses, while showing that Bayesian meta-regression approaches can underestimate risk at clinically critical ranges. The framework generalizes naturally to multivariate systems, offering a rigorous and transparent foundation for evidence synthesis in translational science.</p>

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Translating treatment effects between correlated endpoints

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月28日

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  • Nusrat Rabbee

摘要

Clinical researchers frequently need to answer: 'If a treatment changes endpoint 1 by X%, what change in endpoint 2 should we expect?' Current meta-analytic approaches either produce biased estimates (Daniels-Hughes) or provide correlation measures without explicit translational coefficients. We demonstrate how multivariate random-effects meta-analysis can derive the Expected Translational Association (η), which translates treatment effects across correlated endpoints, simultaneously fitting all correlated endpoints through their shared variance–covariance structure, yielding unbiased estimates and nominal confidence coverage. For convenience, we refer to this stacked implementation as SLIM (Stacked LInear Mixed Effects Model). Extensive simulations demonstrated that SLIM maintains near-zero bias and nominal coverage across scenarios, while the Daniels-Hughes approach exhibits substantial bias due to measurement-error-in-covariates, with bias persisting as sample size increases. Using the abdominal aortic aneurysm (AAA) dataset, we demonstrate how this approach quantifies translational associations between growth-rate and rupture-risk endpoints and enables quantitative biomarker–outcome "what if" analyses, while showing that Bayesian meta-regression approaches can underestimate risk at clinically critical ranges. The framework generalizes naturally to multivariate systems, offering a rigorous and transparent foundation for evidence synthesis in translational science.