MONTE-CARLO METHODS IN CLINICAL RESEARCH - APPLICATIONS IN MULTIVARIABLE ANALYSIS

Background: Monte Carlo methods use ''simulated'' analyses with random numbers for solving problems, particularly those that defy solutions using mathematical theory alone, Research using Monte Carlo simulation s is very popular in many branches of science and is sometimes done in clinical investigation, The origins and basic strategy of the techniq ue, however, may not be well known to clinical researchers, The purpos e of this paper is to describe the history and general principles of M onte Carlo methods and to demonstrate how Monte Carlo simulations were recently applied to examine a phenomenon in multivariable statistical analysis called the number of outcome events per independent variable (EPV), For example, in a cohort of 200 people, with 50 deaths and 5 i ndependent (predictor) variables, EPV = 50/5 = 10, Methods: The ''real -world'' data came from a clinical trial of 673 patients in which 7 va riables were cogent predictors of 252 deaths, so that EPV = 252/7 = 36 , For the Monte Carlo simulations, special models were used while allo wing simulations of proportional hazards and logistic regression to ma intain the basic relationship of variables and the same size of the or iginal population, at EPV values of 2, 5, 10, 15, 20, and 25, Results: The Monte Carlo simulations confirmed a previously undocumented ''rul e of thumb'' stating that when the EPV is less than 10-20, the algebra ic models used in logistic regression and proportional hazards regress ion may be unreliable, leading to imprecise or spurious results, Concl usion: Monte Carlo techniques offer attractive methods for clinical in vestigators to use in solving problems that are not amenable to custom ary mathematical approaches.