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A Note on the Use of Unbiased Estimating Equations to Estimate Correlation in Analysis of Longitudinal Trials
Author(s) -
Sun Wenguang,
Shults Justine,
Leonard Mary
Publication year - 2009
Publication title -
biometrical journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.108
H-Index - 63
eISSN - 1521-4036
pISSN - 0323-3847
DOI - 10.1002/bimj.200710493
Subject(s) - autoregressive model , mathematics , generalized estimating equation , correlation , statistics , estimating equations , outcome (game theory) , markov chain , contrast (vision) , longitudinal data , econometrics , maximum likelihood , computer science , data mining , geometry , mathematical economics , artificial intelligence
Longitudinal trials can yield outcomes that are continuous, binary (yes/no), or are realizations of counts. In this setting we compare three approaches that have been proposed for estimation of the correlation in the framework of generalized estimating equations (GEE): quasi‐least squares (QLS), pseudo‐likelihood (PL), and an approach we refer to as Wang–Carey (WC). We prove that WC and QLS are identical for the first‐order autoregressive AR(1) correlation structure. Using simulations, we then develop guidelines for selection of an appropriate method for analysis of data from a longitudinal trial. In particular, we argue that no method is uniformly superior for analysis of unbalanced and unequally spaced data with a Markov correlation structure. Choice of the best approach will depend on the degree of imbalance and variability in the temporal spacing of measurements, value of the correlation, and type of outcome, e.g. binary or continuous. Finally, we contrast the methods in analysis of a longitudinal study of obesity following renal transplantation in children (© 2009 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)