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Titolo: Analysis of longitudinal Gaussian data with missing data on the response variable
Autore: JacqminGadda, H; Commenges, D; Dartigues, JF;
 Indirizzi:
 INSERM, U330, F33076 Bordeaux, France INSERM Bordeaux France F33076INSERM, U330, F33076 Bordeaux, France
 Titolo Testata:
 REVUE D EPIDEMIOLOGIE ET DE SANTE PUBLIQUE
fascicolo: 6,
volume: 47,
anno: 1999,
pagine: 525  534
 SICI:
 03987620(199912)47:6<525:AOLGDW>2.0.ZU;2P
 Fonte:
 ISI
 Lingua:
 FRE
 Soggetto:
 MINIMENTALSTATE; DROPOUT; MODEL; TIME;
 Keywords:
 ignorable missing data; longitudinal study; non ignorable missing data;
 Tipo documento:
 Article
 Natura:
 Periodico
 Settore Disciplinare:
 Clinical Medicine
 Citazioni:
 15
 Recensione:
 Indirizzi per estratti:
 Indirizzo: JacqminGadda, H INSERM, U330, 146 Rue Leo Saignat, F33076 Bordeaux, France INSERM 146 Rue Leo Saignat Bordeaux France F33076 rance



 Citazione:
 H. JacqminGadda et al., "Analysis of longitudinal Gaussian data with missing data on the response variable", REV EPIDEM, 47(6), 1999, pp. 525534
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 method according to the type of missing responses when the variable under study is gaussian. Method: 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 linens 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 mired effects model for the outcome variable and a logistic model for the probability of response which depends on the outcomevariable. 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 hypothesisof 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. Conclusion: Statistical methods for longitudinal data including non ignorable missing responses are sensitive to hypotheses difficult to verify. Thats, it will be better in practical applications to perform an analysis underthe 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.
ASDD Area Sistemi Dipartimentali e Documentali, Università di Bologna, Catalogo delle riviste ed altri periodici
Documento generato il 14/08/20 alle ore 07:34:48