Department of Biostatistics, University of Arkansas for Medical Sciences, Little Rock, Arkansas 72205, USA.
Radiat Res. 2012 May;177(5):546-54. doi: 10.1667/rr2872.1. Epub 2012 Mar 8.
We present an introduction to, and examples of, Cox proportional hazards regression in the context of animal lethality studies of potential radioprotective agents. This established method is seldom used to analyze survival data collected in such studies, but is appropriate in many instances. Presenting a hypothetical radiation study that examines the efficacy of a potential radioprotectant both in the absence and presence of a potential modifier, we detail how to implement and interpret results from a Cox proportional hazards regression analysis used to analyze the survival data, and we provide relevant SAS® code. Cox proportional hazards regression analysis of survival data from lethal radiation experiments (1) considers the whole distribution of survival times rather than simply the commonly used proportions of animals that survived, (2) provides a unified analysis when multiple factors are present, and (3) can increase statistical power by combining information across different levels of a factor. Cox proportional hazards regression should be considered as a potential statistical method in the toolbox of radiation researchers.
我们介绍了 Cox 比例风险回归在潜在放射性保护剂动物致死性研究中的应用，并提供了一些示例。这种成熟的方法很少用于分析此类研究中收集的生存数据，但在许多情况下是合适的。本文提出了一个假设性的辐射研究，在不存在和存在潜在修饰剂的情况下检查潜在放射性保护剂的功效，详细介绍了如何实现和解释用于分析生存数据的 Cox 比例风险回归分析的结果，并提供了相关的 SAS®代码。从致命性辐射实验中对生存数据进行 Cox 比例风险回归分析（1）考虑了整个生存时间分布，而不仅仅是常用的动物存活比例；（2）当存在多个因素时提供了统一的分析；（3）通过合并不同因素水平的信息可以提高统计效力。Cox 比例风险回归应被视为辐射研究人员工具包中的一种潜在统计方法。
