Deviation from the Proportional Hazards Assumption in Randomized Phase 3 Clinical Trials in Oncology: Prevalence, Associated Factors, and Implications

Abstract

Purpose: Deviations from proportional hazards (DPH), which may be more prevalent in the era of precision medicine and immunotherapy, can lead to under-powered trials or misleading conclusions. We used a meta-analytic approach to estimate DPH across cancer trials, investigate associated factors, and evaluate data-analysis approaches for future trials. Experimental Design: We searched PubMed for phase III trials in breast, lung, prostate, and colorectal cancer published in a pre-selected list of journals between 2014-2016 and extracted individual patient-level data (IPLD) from Kaplan-Meier curves. We re-analyzed IPLD to identify DPH. Potential efficiency gains, when DPHs were present, of alternative statistical methods relative to standard log-rank based analysis were expressed as sample-size requirements for a fixed power level. Results: From 152 trials, we obtained IPLD on 129,401 patients. Among 304 Kaplan-Meier figures, 75 (24.7%) exhibited evidence of DPH, including 8 of 14 (57%) KM pairs from immunotherapy trials. Trial type (immunotherapy, odds ratio (OR) 4.29, 95%CI 1.11-16.6), metastatic patient population (OR 3.18, 95%CI 1.26-8.05), and non-OS endpoints (OR 3.23, 95%CI 1.79-5.88) were associated with DPH. In immunotherapy trials, alternative statistical approaches allowed for more efficient clinical trials with fewer patients (up to 74% reduction) relative to log-rank testing. Conclusions: DPH was found in a notable proportion of time-to-event outcomes in published clinical trials in oncology and was more common for immunotherapy trials and non-OS endpoints. Alternative statistical methods, without proportional hazards assumptions, should be considered in the design and analysis of clinical trials when the likelihood of DPH is high.