IntroductionMixed‐effects models are a popular approach for analysis of longitudinal and/or clustered binary outcomes, with many texts either devoted to or with substantial treatment on the topic (Raudenbush and Bryk, ; Demidenko, ; Skrondal and Rabe‐Hesketh, ; Molenberghs and Verbeke, ; Brown and Prescott, ; Hedeker and Gibbons, ; McCulloch et al., ; Fitzmaurice et al., ; Goldstein, ). Such models are often referred to as belonging to the class of models known as generalized linear mixed models (GLMMs). It is well known that the regression parameters in such models have the “subject‐specific” (SS) or conditional interpretation (Neuhaus et al., ), representing effects of the covariates controlling for, or holding constant, the random effects. This is in contrast to the marginal or “population‐averaged” (PA) estimates of, most notably, the GEE approach (Zeger and Liang, ) and the marginalized multilevel model (Heagerty and Zeger, ). The SS and PA regression parameters generally differ in meaning and value, and the SS regression parameters depend on how many random effects are included in the model (e.g., conditional on random intercepts only or conditional on random intercepts and trends). Thus, Zeger et al. () recommend that SS models are useful when the focus
Biometrics – Wiley
Published: Jan 1, 2018
Keywords: ; ; ; ;
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