title: Analysing repeated measurements with possibly missing observations by modelling marginal distributions.
abstract: Suppose that subjects are observed repeatedly over a common set of time points with possibly time-dependent covariates and possibly missing observations. At each time point we model the marginal distribution of the response variable and the effect of the covariates on that distribution using a class of quasi-likelihood models studied in McCullagh and Nelder. No parametric model of dependence of the repeated observations of the subject is assumed. For large samples, the quasi-likelihood estimates of the time-specific regression coefficients over the set of predetermined time points are shown to be approximately jointly normal. This, coupled with various inference procedures, provides a global picture about the effects of the covariates on the response variable over the entire study period. A lack-of-fit test for testing the adequacy of the assumed quasi-likelihood model is also provided. All the methods considered here are illustrated with real-life examples.
authors.affiliation: Department of Biostatistics, University of Michigan, Ann Arbor 48109.
authors.forename: L J
authors.initials: LJ
authors.lastname: Wei
authors.forename: D O
authors.initials: DO
authors.lastname: Stram
mesh_terms: D000397:Air Pollution; D002635:Chenodeoxycholic Acid / Q000494:pharmacology*; D002648:Child; D002784:Cholesterol / Q000097:blood*; D006801:Humans; D008137:Longitudinal Studies; D008171:Lung Diseases / Q000453:epidemiology*; D008954:Models, Biological; D012044:Regression Analysis; D013223:Statistics as Topic; D013714:Tennessee; D014028:Tobacco Smoke Pollution; D014922:Wisconsin
chemical_list: D014028:Tobacco Smoke Pollution; D002635:Chenodeoxycholic Acid; D002784:Cholesterol