Bayesian interpretation of immunotherapy trials with dynamic treatment effects. (January 2022)

• Record Type: Journal Article
• Authors: Castañon, Eduardo; Sanchez-Arraez, Álvaro; Jimenez-Fonseca, Paula; Alvarez-Manceñido, Felipe; Martínez-Martínez, Irene; Mihic Gongora, Luka; Carmona-Bayonas, Alberto
• Is Part Of: European journal of cancer. Volume 161(2022)
• Page Start: 79
• Page End: 89
• Publication Date: 2022-01
• Subjects: Bayesian models -- Clinical trial -- Dynamic models -- Immunotherapy -- Immune checkpoints inhibitors -- Survival; Cancer -- Periodicals; Neoplasms -- Periodicals; Cancer -- Périodiques; Cancer; Tumors; Electronic journals; Periodicals; Electronic journals; 616.994
• DOI: 10.1016/j.ejca.2021.11.002
• Languages: English
• ISSNs: 0959-8049

Abstract

Abstract: Introduction: The mechanism of action of immune checkpoints inhibitors hinders the writing of rational statistical analysis plans for phase III randomised clinical trials (RCTs) because of their unpredictable dynamic effects. The purpose is to illustrate the advantages of Bayesian reporting of treatment efficacy analysis in immunotherapy RCTs, in contrast to frequentist reporting. Method: Fourteen RCTs (one with two pairwise comparisons) that failed to achieve their primary objective (overall survival, OS) were selected. These RCTs were reanalysed using Bayesian Cox models with dynamic covariate coefficients and time-invariant models. Results: The RCTs that met inclusion criteria were 7 lung cancer trials, various other tumours, with antiPD1, antiPDL1 or antiCTLA4 therapies. The minimum detectable effect (δS ) was superior to the true benefit observed in all cases, in conditions of non-proportional hazards. Schoenfeld tests indicated the existence of PH assumption violations (p<0.05) in 6/15 cases. The Bayesian Cox models revealed a probability of benefit >79% in all the RCTs, with the therapeutic equivalence hypothesis unlikely. The OS curves diverged after a median of 9.1 months. Since the divergency, no non-proportionality was evinced in 13/15, while the Wald tests achieved p<0.05 in 12/15 datasets. In all cases, the Bayesian Cox models with dynamic coefficients detected fluctuations of the hazard ratio, and increased 2-year OS was the most likely hypothesis. Conclusion: We recommend progressively implementing Bayesian and dynamic analyses in all RCTs of immunotherapy to interpret and assess the credibility of frequentist results. Highlights: Proportional hazards (PH) assumption violations are common in immunotherapy trials. These dynamic patterns are hard to anticipate and depict in frequentist designs. Prespecified minimum detectable effects require the PH assumption to be sustained. Integrated Bayesian dynamic reporting is useful to interpret immunotherapy trials.