Diagnostic tools for random effects in the repeated measures growth curve model

✍ Scribed by P.J Lindsey; J.K Lindsey

• Book ID: 104306956
• Publisher: Elsevier Science
• Year: 2000
• Tongue: English
• Weight: 357 KB
• Volume: 33
• Category: Article
• ISSN: 0167-9473
• DOI: 10.1016/s0167-9473(99)00049-3

✦ Synopsis

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 ÿtted to describe the mean proÿles under di erent treatments. The dependence among responses from the same individuals is generally handled by a random e ects 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 e ects models, and their in uence on inferences about treatment e ects, 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.