CBS 2019
CBSMD教育中心
中 文

Other Relevant Articles

Abstract

Recommended Article

Multimodality imaging in cardiology: a statement on behalf of the Task Force on Multimodality Imaging of the European Association of Cardiovascular Imaging Cardiovascular Risk Reduction with Icosapent Ethyl for Hypertriglyceridemia Incidence of contrast-induced acute kidney injury in a large cohort of all-comers undergoing percutaneous coronary intervention: Comparison of five contrast media Society of cardiac angiography and interventions: suggested management of the no-reflow phenomenon in the cardiac catheterization laboratory 2017 ESC Guidelines on the Diagnosis and Treatment of Peripheral Arterial Diseases, in collaboration with the European Society for Vascular Surgery (ESVS): Document covering atherosclerotic disease of extracranial carotid and vertebral, mesenteric, renal, upper and lower extremity arteries Endorsed Long-Term Effect of Ultrathin-Strut Versus Thin-Strut Drug-Eluting Stents in Patients With Small Vessel Coronary Artery Disease Undergoing Percutaneous Coronary Intervention: A Subgroup Analysis of the BIOSCIENCE Randomized Trial Predicting the 10-Year Risks of Atherosclerotic Cardiovascular Disease in Chinese Population: The China-PAR Project (Prediction for ASCVD Risk in China) Classification of Deaths in Cardiovascular Outcomes Trials Known Unknowns and Unknown Unknowns

Volume 74, Issue 16, October 2019

JOURNAL:J Am Coll Cardiol. Article Link

Nonproportional Hazards for Time-to-Event Outcomes in Clinical Trials: JACC Review Topic of the Week

J Gregson, L Sharples, GW Stone et al. Keywords: clinical trials; Cox proportional hazards; nonproportional hazards; statistics; time-to-event outcomes; trial design

ABSTRACT


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.