Longitudinal data analysis. A comparison between generalized estimating equations and random coefficient analysis

The analysis of data from longitudinal studies requires special techniques, which take into account the fact that the repeated measurements within one individual are correlated. In this paper, the two most commonly used techniques to analyze longitudinal data are compared: generalized estimating equations (GEE) and random coefficient analysis. Both techniques were used to analyze a longitudinal dataset with six measurements on 147 subjects. The purpose of the example was to analyze the relationship between serum cholesterol and four predictor variables, i.e., physical fitness at baseline, body fatness (measured by sum of the thickness of four skinfolds), smoking and gender. The results showed that for a continuous outcome variable, GEE and random coefficient analysis gave comparable results, i.e., GEE-analysis with an exchangeable correlation structure and random coefficient analysis with only a random intercept were the same. There was also no difference between both techniques in the analysis of a dataset with missing data, even when the missing data was highly selective on earlier observed data. For a dichotomous outcome variable, the magnitude of the regression coefficients and standard errors was higher when calculated with random coefficient analysis then when calculated with GEE-analysis. Analysis of a dataset with missing data with a dichotomous outcome variable showed unpredictable results for both GEE and random coefficient analysis. It can be concluded that for a continuous outcome variable, GEE and random coefficient analysis are comparable. Longitudinal data-analysis with dichotomous outcome variables should, however, be interpreted with caution, especially when there are missing data.