Net benefit in the presence of correlated prioritized outcomes using generalized pairwise comparisons: A simulation study. (2nd November 2020)

• Record Type: Journal Article
• Authors: Giai, Joris; Maucort‐Boulch, Delphine; Ozenne, Brice; Chiêm, Jean‐Christophe; Buyse, Marc; Péron, Julien
• Is Part Of: Statistics in medicine. Volume 40:Number 3(2021)
• Page Start: 553
• Page End: 565
• Publication Date: 2020-11-02
• Subjects: clinical trial -- correlation -- generalized pairwise comparisons -- multivariate analysis -- net benefit; Medical statistics -- Periodicals; Statistique médicale -- Périodiques; Statistiques médicales -- Périodiques; 610.727
• DOI: 10.1002/sim.8788
• Languages: English
• ISSNs: 0277-6715

Abstract

Abstract : Background: The prioritized net benefit (Δ) is a measure of the benefit‐risk balance in clinical trials, based on generalized pairwise comparisons (GPC) using several prioritized outcomes. Its estimation requires the classification as Wins or Losses of all possible pairs of patients, one from the experimental treatment (E) group and one from the control treatment (C) group. In this simulation study, we assessed the impact of the correlation between prioritized outcomes on Δ, its estimate, bias, size, and power. Methods: The theoretical Δ value was derived for the specific case of two correlated binary outcomes when a normal copula is used. Focusing on one efficacy and one toxicity outcome, two situations frequently met in practice were simulated: binary efficacy outcome with binary toxicity outcome, or time to event efficacy outcome with categorical toxicity outcome. Several scenarios of efficacy and toxicity were generated, with various levels of correlation. Results: When E was more effective than C, positive correlations were mainly associated with a decrease in the proportion of Losses, while negative correlations were associated with a decrease in the proportion of Wins on the toxicity outcome. This resulted in an increase of Δ ^ with the intensity of the positive correlation without adding any bias. Results were similar whatever the type of outcomes generated but led to power alteration. Conclusion: Correlations between outcomes analyzed with GPC led to substantial but predictable modifications of Δ and its estimate. Correlations should be taken into consideration when performing sample size estimations in clinical trials.