Critical review of oncology clinical trial design under non-proportional hazards. (June 2021)

• Record Type: Journal Article
• Authors: Ananthakrishnan, Revathi; Green, Stephanie; Previtali, Alessandro; Liu, Rong; Li, Daniel; LaValley, Michael
• Is Part Of: Critical reviews in oncology/hematology. Volume 162(2021)
• Publication Date: 2021-06
• Subjects: Non-proportional hazards -- Oncology trials -- Delayed treatment effects -- Diminishing treatment effects -- Crossing hazards -- Long-term survivors -- Time-to-event endpoint; Oncology -- Periodicals; Hematology -- Periodicals; 616.994
• DOI: 10.1016/j.critrevonc.2021.103350
• Languages: English
• ISSNs: 1040-8428

Abstract

Graphical abstract: In this comprehensive review article, we describe methods to design a randomized oncology trial, calculate the sample size, analyze the trial data and obtain summary measures of treatment effect in the presence of non-PH. Highlights: Non-proportional hazards are seen in time-to-event endpoints in oncology trials. Patterns: delayed effect, diminishing effect, crossing hazards, long-term survivors. We have reviewed various methods to deal with non-proportional hazards such as: Delayed effect, RMST, max-combo, Yang-Prentice, parametric & cure rate models. Suggest designing trial using non-proportional hazards methods for large deviation. Abstract: In trials of novel immuno-oncology drugs, the proportional hazards (PH) assumption often does not hold for the primary time-to-event (TTE) efficacy endpoint, likely due to the unique mechanism of action of these drugs. In practice, when it is anticipated that PH may not hold for the TTE endpoint with respect to treatment, the sample size is often still calculated under the PH assumption, and the hazard ratio (HR) from the Cox model is still reported as the primary measure of the treatment effect. Sensitivity analyses of the TTE data using methods that are suitable under non-proportional hazards (non-PH) are commonly pre-planned. In cases where a substantial deviation from the PH assumption is likely, we suggest designing the trial, calculating the sample size and analyzing the data, using a suitable method that accounts for non-PH, after gaining alignment with regulatory authorities. In this comprehensive review article, we describe methods to design a randomized oncology trial, calculate the sample size, analyze the trial data and obtain summary measures of the treatment effect in the presence of non-PH. For each method, we provide examples of its use from the recent oncology trials literature. We also summarize in the Appendix some methods to conduct sensitivity analyses for overall survival (OS) when patients in a randomized trial switch or cross-over to the other treatment arm after disease progression on the initial treatment arm, and obtain an adjusted or weighted HR for OS in the presence of cross-over. This is an example of the treatment itself changing at a specific point in time - this cross-over may lead to a non-PH pattern of diminishing treatment effect.