title: [Analysis of longitudinal Gaussian data with missing data on the response variable].
abstract: BACKGROUND
  Using an application and a simulation study we show the bias induced by missing data in the outcome in longitudinal studies and discuss suitable statistical methods according to the type of missing responses when the variable under study is gaussian.
  
  
  METHODS
  The model used for the analysis of gaussian longitudinal data is the mixed effects linear model. When the probability of response does not depend on the missing values of the outcome and on the parameters of the linear model, missing data are ignorable, and parameters of the mixed effects linear model may be estimated by the maximum likelihood method with classical softwares. When the missing data are non ignorable, several methods have been proposed. We describe the method proposed by Diggle and Kenward (1994) (DK method) for which a software is available. This model consists in the combination of a linear mixed effects model for the outcome variable and a logistic model for the probability of response which depends on the outcome variable.
  
  
  RESULTS
  A simulation study shows the efficacy of this method and its limits when the data are not normal. In this case, estimators obtained by the DK approach may be more biased than estimators obtained under the hypothesis of ignorable missing data even if the data are non ignorable. Data of the Paquid cohort about the evolution of the scores to a neuropsychological test among elderly subjects show the bias of a naive analysis using all available data. Although missing responses are not ignorable in this study, estimates of the linear mixed effects model are not very different using the DK approach and the hypothesis of ignorable missing data.
  
  
  CONCLUSIONS
  Statistical methods for longitudinal data including non ignorable missing responses are sensitive to hypotheses difficult to verify. Thus, it will be better in practical applications to perform an analysis under the hypothesis of ignorable missing responses and compare the results obtained with several approaches for non ignorable missing data. However, such a strategy requires development of new softwares.
authors.affiliation: INSERM U330, 146 rue Léo Saignat, 33076 Bordeaux Cedex.
authors.forename: H
authors.initials: H
authors.lastname: Jacqmin-Gadda
authors.forename: D
authors.initials: D
authors.lastname: Commenges
authors.forename: J
authors.initials: J
authors.lastname: Dartigues
mesh_terms: D003627:Data Interpretation, Statistical; D008137:Longitudinal Studies; D015233:Models, Statistical; D016011:Normal Distribution