Abstract

<div>AbstractPurpose:<p>Deviations from proportional hazards (DPHs), which may be more prevalent in the era of precision medicine and immunotherapy, can lead to underpowered trials or misleading conclusions. We used a meta-analytic approach to estimate DPHs across cancer trials, investigate associated factors, and evaluate data-analysis approaches for future trials.</p><p><b>Experimental Design:</b> We searched PubMed for phase III trials in breast, lung, prostate, and colorectal cancer published in a preselected list of journals between 2014 and 2016 and extracted individual patient-level data (IPLD) from Kaplan–Meier curves. We re-analyzed IPLD to identify DPHs. 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.</p>Results:<p>From 152 trials, we obtained IPLD on 129,401 patients. Among 304 Kaplan–Meier figures, 75 (24.7%) exhibited evidence of DPHs, including eight of 14 (57%) KM pairs from immunotherapy trials. Trial type [immunotherapy, odds ratio (OR), 4.29; 95% confidence interval (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 DPHs. In immunotherapy trials, alternative statistical approaches allowed for more efficient clinical trials with fewer patients (up to 74% reduction) relative to log-rank testing.</p>Conclusions:<p>DPHs were 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 DPHs is high.</p></div>

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