Crouchley, Rob and Davies, R. B. (1999) A comparison of population average and random-effect models for the analysis of longitudinal count data with base-line information. Journal of the Royal Statistical Society, Series A, 162 (3). pp. 331-347.Full text not available from this repository.
The generalized estimating equation (GEE) approach to the analysis of longitudinal data has many attractive robustness properties and can provide a 'population average' characterization of interest, for example, to clinicians who have to treat patients on the basis of their observed characteristics. However, these methods have limitations which restrict their usefulness in both the social and the medical sciences. This conclusion is based on the premise that the main motivations for longitudinal analysis are insight into microlevel dynamics and improved control for omitted or unmeasured variables. We claim that to address these issues a properly formulated random-effects model is required. In addition to a theoretical assessment of some of the issues, we illustrate this by reanalysing data on polyp counts. In this example, the covariates include a base-line outcome, and the effectiveness of the treatment seems to vary by base-line. We compare the random-effects approach with the GEE approach and conclude that the GEE approach is inappropriate for assessing the treatment effects for these data.
|Journal or Publication Title:||Journal of the Royal Statistical Society, Series A|
|Uncontrolled Keywords:||Base-line data • Consistency • Count data • Endogenous variables • Generalized estimating equations • Marginal models • Random effects|
|Subjects:||Q Science > QA Mathematics|
|Departments:||Faculty of Science and Technology > Mathematics and Statistics|
Faculty of Science and Technology > Lancaster Environment Centre
|Deposited On:||07 Nov 2008 20:33|
|Last Modified:||26 Jul 2012 15:34|
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