IMPORTANCE OF EVENTS PER INDEPENDENT VARIABLE IN PROPORTIONAL HAZARDSANALYSIS .1. BACKGROUND, GOALS, AND GENERAL STRATEGY

Multivariable methods of analysis can yield problematic results if met hodological guidelines and mathematical assumptions are ignored. A pro blem arising from a too-small ratio of events per variable (EPV) can a ffect the accuracy and precision of regression coefficients and their tests of statistical significance. The problem occurs when a proportio nal 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 propor tional hazards analysis done with Monte Carlo simulations in an empiri cal data set of 673 subjects enrolled in a multicenter trial of corona ry artery bypass surgery. The research is presented in two parts: Part I describes the data set and strategy used for the analyses, includin g the Monte Carlo simulation studies done to determine and compare the impact of various values of EPV in proportional hazards analytical re sults. Part II compares the output of regression models obtained from the simulations, and discusses the implication of the findings.