Diagnostic tools from random effects in the repreated measures growth curve model

Abstract

Growth curve models assuming a normal distribution are often used in repeated measurements applications because of the wide availability of software. In many standard situations, a polynomial in time is fitted to describe the mean profiles under different treatments. The dependence among responses from the same individuals is generally handled by a random effects model, although an auto-regressive structure can often be more appropriate. We consider both, in the context of missing observations. We present diagnostics for two major problems: (1) the forms of the mixing distribution in random effects models, and their influence on inferences about treatment effects, and (2) the randomness of missing observations. To demonstrate the utility of our techniques, we reanalyze data on percentage protein content in milk, often erroneously analyzed as illustrating a dropout phenomenon