Deviation from the proportional hazards assumption in randomized phase 3 clinical trials in oncology: Prevalence, associated factors, and implications

Rifaquat Rahman, Geoffrey Fell, Steffen Ventz, Andrea Arfé, Alyssa M. Vanderbeek, Lorenzo Trippa, Brian M. Alexander

Research output: Contribution to journalArticlepeer-review

47 Scopus citations


Purpose: 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. Experimental Design: 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. Results: 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. Conclusions: 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.

Original languageEnglish (US)
Pages (from-to)6339-6345
Number of pages7
JournalClinical Cancer Research
Issue number21
StatePublished - Nov 1 2019
Externally publishedYes

Bibliographical note

Funding Information:
This work was supported by the Burroughs Wellcome Innovations in Regulatory Science Award.

Publisher Copyright:
© 2019 American Association for Cancer Research.


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