title: Model choice can obscure results in longitudinal studies.
abstract: BACKGROUND
  This article examines how different parameterizations of age and time in modeling observational longitudinal data can affect results.
  
  
  METHODS
  When individuals of different ages at study entry are considered, it becomes necessary to distinguish between longitudinal and cross-sectional differences to overcome possible selection biases.
  
  
  RESULTS
  Various models were fitted using data from longitudinal studies with participants with different ages and different follow-up lengths. Decomposing age into two components-age at entry into the study (first age) and the longitudinal follow-up (time) compared with considering age alone-leads to different conclusions.
  
  
  CONCLUSIONS
  In general, models using both first age and time terms performed better, and these terms are usually necessary to correctly analyze longitudinal data.
authors.affiliation: Gerontology Research Center, National Institute on Aging, 5600 Nathan Shock Drive, Baltimore, MD 21224, USA.
authors.forename: Christopher H
authors.initials: CH
authors.lastname: Morrell
authors.forename: Larry J
authors.initials: LJ
authors.lastname: Brant
authors.forename: Luigi
authors.initials: L
authors.lastname: Ferrucci
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D000375:Aging / Q000502:physiology*; D001835:Body Weight / Q000502:physiology*; D002784:Cholesterol / Q000378:metabolism*; D003430:Cross-Sectional Studies; D004812:Epidemiologic Methods; D006801:Humans; D016014:Linear Models; D008137:Longitudinal Studies; D015233:Models, Statistical; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013997:Time Factors
chemical_list: D002784:Cholesterol