Importance of events per independent variable in proportional hazards analysis I. Background, goals, and general strategy

• Published in: Journal of clinical epidemiology 1995-12, Vol.48 (12), p.1495-1501
• Main Authors: Concato, John, Peduzzi, Peter, Holford, Theodore R., Feinstein, Alvan R.
• Format: Article
• Language: English
• Subjects: Biological and medical sciences; Computer Simulation; Connecticut - epidemiology; Coronary Artery Bypass; Coronary Disease - mortality; Coronary Disease - surgery; Epidemiology; General aspects; Humans; Medical sciences; Methodology; Monte Carlo Method; Proportional Hazards Models; Public health. Hygiene; Public health. Hygiene-occupational medicine; Risk Factors; Survival Rate
• ISSN: 0895-4356; 1878-5921
• DOI: 10.1016/0895-4356(95)00510-2

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.