Key result
Alternative statistical models are recommended over standard Cox proportional hazards when nonproportional hazards occur.
Why the study?
Nonproportional hazards frequently occur in cardiology clinical trials, necessitating alternative analysis strategies beyond the Cox proportional hazards model.
This review highlights the importance of recognizing nonproportional hazards in cardiology trials and provides recommendations for alternative statistical analysis strategies.
Most major clinical trials in cardiology report time-to-event outcomes using the Cox proportional hazards model so that a treatment effect is estimated as the hazard ratio between groups, accompanied by its 95% confidence interval and a log-rank p value. But nonproportionality of hazards (non-PH) over time occurs quite often, making alternative analysis strategies appropriate. This review presents real examples of cardiology trials with different types of non-PH: an early treatment effect, a late treatment effect, and a diminishing treatment effect. In such scenarios, the relative merits of a Cox model, an accelerated failure time model, a milestone analysis, and restricted mean survival time are examined. Some post hoc analyses for exploring any specific pattern of non-PH are also presented. Recommendations are made, particularly regarding how to handle non-PH in pre-defined Statistical Analysis Plans, trial publications, and regulatory submissions.
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Gregson et al. (2019) conducted a review in Cardiovascular disease. Alternative analysis strategies, such as accelerated failure time models and restricted mean survival time, are recommended when nonproportional hazards occur in cardiology clinical trials.
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