CBS 2019
CBSMD教育中心
中 文

Other Relevant Articles

Abstract

Recommended Article

Older Adults in the Cardiac Intensive Care Unit: Factoring Geriatric Syndromes in the Management, Prognosis, and Process of Care: A Scientific Statement From the American Heart Association Appropriate Use Criteria and Health Status Outcomes Following Chronic Total Occlusion Percutaneous Coronary Intervention: Insights From the OPEN-CTO Registry Systemic microvascular dysfunction in microvascular and vasospastic angina Digital learning and the future cardiologist Coronary flow velocity reserve predicts adverse prognosis in women with angina and noobstructive coronary artery disease: resultsfrom the iPOWER study Generalizing Intensive Blood Pressure Treatment to Adults With Diabetes Mellitus Mechanisms and diagnostic evaluation of persistent or recurrent angina following percutaneous coronary revascularization The Future of Cardiovascular Computed Tomography Advanced Analytics and Clinical Insights

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.