Importance of events per independent variable in proportional hazards analysis .1. Background, goals, and general strategy
Importance of events per independent variable in proportional hazards analysis .1. Background, goals, and general strategy
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DOI：
10.1016/0895-4356(95)00510-2
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发表时间：
1995-12-01
影响因子：
7.2
通讯作者：
Feinstein, AR
中科院分区：
文献类型：
--
作者：
Concato, J;Peduzzi, P;Feinstein, AR
如果忽略方法指南和数学假设，多变量分析方法可能会产生有问题的结果。由于每个变量的事件比率 (EPV) 太小而产生的问题可能会影响回归系数及其统计显着性检验的准确度和精密度。当比例风险分析包含的“失败”事件（例如死亡）相对于所包含的自变量数量太少时，就会出现问题。在当前的研究中，在参加冠状动脉搭桥手术多中心试验的 673 名受试者的经验数据集中，通过蒙特卡罗模拟进行比例风险分析结果来评估 EPV 的影响。该研究分为两部分：第一部分描述了用于分析的数据集和策略，包括为确定和比较比例风险分析结果中不同 EPV 值的影响而进行的蒙特卡罗模拟研究。第二部分比较了从模拟中获得的回归模型的输出，并讨论了研究结果的含义。
Multivariable methods of analysis can yield problematic results if methodological guidelines and mathematical assumptions are ignored. A problem arising from a too-small ratio of events per variable (EPV) can affect the accuracy and precision of regression coefficients and their tests of statistical significance. The problem occurs when a proportional hazards analysis contains too few ''failure'' events (e.g., deaths) in relation to the number of included independent variables. In the current research, the impact of EPV was assessed for results of proportional hazards analysis done with Monte Carlo simulations in an empirical data set of 673 subjects enrolled in a multicenter trial of coronary artery bypass surgery.The research is presented in two parts: Part I describes the data set and strategy used for the analyses, including the Monte Carlo simulation studies done to determine and compare the impact of various values of EPV in proportional hazards analytical results. Part II compares the output of regression models obtained from the simulations, and discusses the implication of the findings.