title: Fitting growth curve models to longitudinal data with missing observations.
abstract: We discuss the analysis of growth curve data with missing or incomplete information. The approach is to fit subject-specific models and then to carry out an analysis in terms of the estimated parameters. This achieves reduction of data and eliminates the need for special considerations for subjects with missing data. Although there is no perfect substitute for complete data, our approach provides a way to handle missing data using a straightforward application of well-known statistical methodology.
authors.affiliation: Department of Biometry and Genetics, Louisiana State University Medical Center, New Orleans 70112.
authors.forename: W D
authors.initials: WD
authors.lastname: Johnson
authors.forename: V T
authors.initials: VT
authors.lastname: George
authors.forename: A
authors.initials: A
authors.lastname: Shahane
authors.forename: G J
authors.initials: GJ
authors.lastname: Fuchs
mesh_terms: D002508:Cephalometry; D002648:Child; D003625:Data Collection / Q000592:standards*; D005260:Female; D006128:Growth; D006801:Humans; D016018:Least-Squares Analysis; D016013:Likelihood Functions; D016014:Linear Models; D008137:Longitudinal Studies; D008297:Male; D008445:Maxillofacial Development; D011502:Protein-Energy Malnutrition / Q000453:epidemiology