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

TLDR

Multivariable analyses can give misleading results when methodological assumptions are ignored, especially when the ratio of events to independent variables is too low, which compromises the accuracy and significance testing of regression coefficients. The study used Monte Carlo simulations on 673 patients from a multicenter coronary artery bypass trial to evaluate how different event‑per‑variable ratios affect proportional hazards models, detailing the dataset and analytic strategy. Simulation results showed that low EPV distorts regression outputs, underscoring the need for adequate event counts to ensure reliable proportional hazards analysis.

Abstract

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.