Journal
JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION
Volume 88, Issue 3, Pages 533-552Publisher
TAYLOR & FRANCIS LTD
DOI: 10.1080/00949655.2017.1397151
Keywords
Data-generating process; survival analysis; proportional hazards model; simulations; Monte Carlo simulations; power and sample size calculation
Funding
- Institute for Clinical Evaluative Sciences (ICES)
- Ontario Ministry of Health and Long-Term Care (MOHLTC)
- Canadian Institutes of Health Research (CIHR) [MOP 86508]
- Heart and Stroke Foundation of Canada
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The use of the Cox proportional hazards regression model is wide-spread. A key assumption of the model is that of proportional hazards. Analysts frequently test the validity of this assumption using statistical significance testing. However, the statistical power of such assessments is frequently unknown. We used Monte Carlo simulations to estimate the statistical power of two different methods for detecting violations of this assumption. When the covariate was binary, we found that a model-based method had greater power than a method based on cumulative sums of martingale residuals. Furthermore, the parametric nature of the distribution of event times had an impact on power when the covariate was binary. Statistical power to detect a strong violation of the proportional hazards assumption was low to moderate even when the number of observed events was high. In many data sets, power to detect a violation of this assumption is likely to be low to modest.
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