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

Jos W.R. Twisk*, Wieke de Vente, Adri T. Apeldoorn, Michiel R. de Boer

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

Background: Within epidemiology both mixed model analysis and 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. Conclusion: 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).

Original languageEnglish
Pages (from-to)e12613-1-e12613-8
JournalEpidemiology Biostatistics and Public Health
Volume14
Issue number3
DOIs
Publication statusPublished - 2017

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