4.5 Article

Impact of correlations between prioritized outcomes on the net benefit and its estimate by generalized pairwise comparisons

Journal

STATISTICS IN MEDICINE
Volume 42, Issue 10, Pages 1606-1624

Publisher

WILEY
DOI: 10.1002/sim.9690

Keywords

clinical trial; generalized pairwise comparisons; net benefit; prioritized outcomes; risk-benefit

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This study examined the impact of correlations between variables on the net benefit values and estimates in clinical trials. The results showed that the correlations could affect the net benefit in either a positive or negative direction, and a corrective method was proposed to mitigate this bias.
Benefit-risk balance is gaining interest in clinical trials. For the comprehensive assessment of benefits and risks, generalized pairwise comparisons are increasingly used to estimate the net benefit based on multiple prioritized outcomes. Although previous research has demonstrated that the correlations between the outcomes impact the net benefit and its estimate, the direction and magnitude of this impact remain unclear. In this study, we investigated the impact of correlations between two binary or Gaussian variables on the true net benefit values via theoretical and numerical analyses. We also explored the impact of correlations between survival and categorical variables on the net benefit estimates based on four existing methods (Gehan, Peron, Gehan with correction, and Peron with correction) in the presence of right censoring via simulation and application to actual oncology clinical trial data. Our theoretical and numerical analyses revealed that the true net benefit values were impacted by the correlations in various directions depending on the outcome distributions. With binary endpoints, this direction was governed by a simple rule with a threshold of 50% for a favorable outcome. Our simulation showed that the net benefit estimates based on Gehan's or Peron's scoring rule could be substantially biased in the presence of right censoring, and that the direction and magnitude of this bias were associated with the outcome correlations. The recently proposed correction method greatly reduced this bias, even in the presence of strong outcome correlations. The impact of correlations should be carefully considered when interpreting the net benefit and its estimate.

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