Individualized Net Benefit estimation and meta‐analysis using generalized pairwise comparisons in N‐of‐1 trials. (3rd January 2023)

• Record Type: Journal Article
• Authors: Giai, Joris; Péron, Julien; Roustit, Matthieu; Cracowski, Jean‐Luc; Roy, Pascal; Ozenne, Brice; Buyse, Marc; Maucort‐Boulch, Delphine
• Is Part Of: Statistics in medicine. Volume 42:Number 6(2023)
• Page Start: 878
• Page End: 893
• Publication Date: 2023-01-03
• Subjects: generalized pairwise comparisons -- meta‐analyzes -- net benefit -- N‐of‐1 trials -- personalized medicine; Medical statistics -- Periodicals; Statistique médicale -- Périodiques; Statistiques médicales -- Périodiques; 610.727
• DOI: 10.1002/sim.9648
• Languages: English
• ISSNs: 0277-6715

Abstract

Abstract : Background: The Net Benefit (Δ) is a measure of the benefit‐risk balance in clinical trials, based on generalized pairwise comparisons (GPC) using several prioritized outcomes and thresholds of clinical relevance. We extended Δ to N‐of‐1 trials, with a focus on patient‐level and population‐level Δ. Methods: We developed a Δ estimator at the individual level as an extension of the stratum‐specific Δ, and at the population‐level as an extension of the stratified Δ. We performed a simulation study mimicking PROFIL, a series of 38 N‐of‐1 trials testing sildenafil in Raynaud's phenomenon, to assess the power for such an analysis with realistic data. We then reanalyzed PROFIL using GPC. This reanalysis was finally interpreted in the context of the main analysis of PROFIL which used Bayesian individual probabilities of efficacy. Results: Simulations under the null showed good size of the test for both individual and population levels. The test lacked power when being simulated from the true PROFIL data, even when increasing the number of repetitions up to 140 days per patient. PROFIL individual‐level estimated Δ were well correlated with the probabilities of efficacy from the Bayesian analysis while showing similarly wide confidence intervals. Population‐level estimated Δ was not significantly different from zero, consistently with the previous Bayesian analysis. Conclusion: GPC can be used to estimate individual Δ which can then be aggregated in a meta‐analytic way in N‐of‐1 trials. GPC ability to easily incorporate patient preferences allow for more personalized treatment evaluation, while needing much less computing time than Bayesian modeling.