A Family of Generalized Linear Models for Repeated Measures with Normal and Conjugate Random Effects

TLDR: In this paper, a broad class of generalized linear models accommodating overdispersion and clustering through two separate sets of random effects is proposed, including conjugate random effects at the level of the mean and normal random effects embedded within the linear pre- dictor.

Abstract: Non-Gaussian outcomes are often modeled using members of the so-called exponential family. Notorious members are the Bernoulli model for binary data, leading to logistic regression, and the Poisson model for count data, leading to Poisson regression. Two of the main reasons for extending this family are (1) the occurrence of overdispersion, meaning that the vari- ability in the data is not adequately described by the models, which often exhibit a prescribed mean-variance link, and (2) the accommodation of hier- archical structure in the data, stemming from clustering in the data which, in turn, may result from repeatedly measuring the outcome, for various mem- bers of the same family, etc. The first issue is dealt with through a variety of overdispersion models, such as, for example, the beta-binomial model for grouped binary data and the negative-binomial model for counts. Clustering is often accommodated through the inclusion of random subject-specific ef- fects. Though not always, one conventionally assumes such random effects to be normally distributed. While both of these phenomena may occur simul- taneously, models combining them are uncommon. This paper proposes a broad class of generalized linear models accommodating overdispersion and clustering through two separate sets of random effects. We place particular emphasis on so-called conjugate random effects at the level of the mean for the first aspect and normal random effects embedded within the linear pre- dictor for the second aspect, even though our family is more general. The binary, count and time-to-event cases are given particular emphasis. Apart from model formulation, we present an overview of estimation methods, and then settle for maximum likelihood estimation with analytic-numerical in- tegration. Implications for the derivation of marginal correlations functions are discussed. The methodology is applied to data from a study in epileptic seizures, a clinical trial in toenail infection named onychomycosis and sur- vival data in children with asthma.