Should we use logistic mixed model analysis for the effect estimation in a longitudinal RCT with a dichotomous outcome variable?

Jos Twisk,Adri Apeldoorn,Michiel R De Boer,Wieke De Vente

doi:10.2427/12613

Abstract

Background: Within epidemiology both mixed model analysis and Generalised Estimating Equations (GEE analysis) are frequently used to analyse longitudinal RCT data. With a continuous outcome, both methods lead to more or less the same results, but with a dichotomous outcome the results are totally different. The purpose of the present study is to evaluate the performance of a logistic mixed model analysis and a logistic GEE analysis and to give an advice which of the two methods should be used. Methods: Two real life RCT datasets with and without missing data were used to perform this evaluation. Regarding the logistic mixed model analysis also two different estimation procedures were compared to each other. Results: The regression coefficients obtained from the two logistic mixed model analyses were different from each other, but were always higher then the regression coefficients derived from a logistic GEE analysis. Because this also holds for the standard errors, the corresponding p-values were more or less the same. It was further shown that the effect estimates derived from a logistic mixed model analysis were an overestimation of the ‘real’ effect estimates. Conclusions: Although logistic mixed model analysis is widely used for the analysis of longitudinal RCT data, this article shows that logistic mixed model analysis should not be used when one is interested in the magnitude of the regression coefficients (i.e. effect estimates).

Highlights

Within epidemiology, the two most frequently used methods to analyse longitudinal data from a ramdomised controlled trial (RCT) are Generalised Estimating Equations (GEE analysis) and mixed model analysis
Conclusion: logistic mixed model analysis is widely used for the analysis of longitudinal RCT data, this article shows that logistic mixed model analysis should not be used when one is interested in the magnitude of the regression coefficients
GEE analysis is known as a ‘population average’ approach, while mixed model analysis is known as a ‘subject specific’ approach [5]. This does not influence the values of the estimated regression coefficients obtained from a linear GEE analysis and a linear mixed mode analysis, but it does influence the values of the estimated regression coefficients obtained from a logistic GEE analysis and a logistic mixed model analysis

Summary

Introduction

The two most frequently used methods to analyse longitudinal data from a ramdomised controlled trial (RCT) are Generalised Estimating Equations (GEE analysis) and mixed model analysis. The difference in regression coefficients is a theoretical one, which is always in favor of a mixed model analysis, meaning that the regression coefficients obtained from a logistic mixed model analysis will always be higher (i.e. further away from zero) compared to the regression coefficients obtained from a logistic GEE analysis This difference is based on a mathematical relationship and depends on the magnitude of the between subject variance (see equation 1) [6,7]. When there is more between subject variance, the difference between the regression coefficients will be larger Within epidemiology both mixed model analysis and GEE analysis are frequently used to analyse longitudinal RCT data. Conclusion: logistic mixed model analysis is widely used for the analysis of longitudinal RCT data, this article shows that logistic mixed model analysis should not be used when one is interested in the magnitude of the regression coefficients (i.e. effect estimates)

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