Repeated measures analysis of binary outcomes: applications to injury research

Repeated measures are reasonably common in injury research and thus tools are required for appropriate analysis in order to account for the correlated nature of this type of data. Three methods for analyzing repeated measures binary outcome data are presented and contrasted: generalized estimating equations (GEE), a survey sample methodology, and logistic regression. These methods are applied to data collected from a cohort study of rugby players, designed to examine the risk and protective factors for rugby injury. It is not, however, the purpose of this paper to present causal models of rugby injuries. The GEE approach is attractive because it is able to account for the correlation among a subject's outcomes and several covariates can be included in a model. The survey sample method approach, which also accounts for the correlation but is restrictive in terms of the number of covariates it can handle, is another approach which is described. These two methods are contrasted to logistic regression, which assumes independence among a subject's outcomes. Under certain circumstances, the three methods do not differ substantially from one another. Under other circumstances, since logistic regression ignores the correlated nature of the data, standard errors may be incorrectly estimated and thus certain covariates may be incorrectly identified as significant predictors in a model.