Titre :
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Longitudinal Data Analysis for Generalized Linear Models Under Participant-Driven Informative Follow-up : An Application in Maternal Health Epidemiology. (2010)
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Auteurs :
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Petra BUZKOVA ;
Elizabeth-R BROWN ;
JOHN-STEWART (Grace-C) : USA. Department of Medicine. School of Medicine. University of Washington. Seattle. WA.
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Type de document :
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Article
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Dans :
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American journal of epidemiology (vol. 171, n° 2, 2010)
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Pagination :
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189-197
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Langues:
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Anglais
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Mots-clés :
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Analyse donnée
;
Participation
;
Grossesse
;
Mère
;
Epidémiologie
;
Interprétation
;
Statistique
;
Méthodologie
;
Homme
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Résumé :
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[BDSP. Notice produite par INIST-CNRS R0x7FoJ9. Diffusion soumise à autorisation]. It is common in longitudinal studies for scheduled visits to be accompanied by as-needed visits due to medical events occurring between scheduled visits. If the timing of these as-needed visits is related to factors that are associated with the outcome but are not among the regression model covariates, naively including these as-needed visits in the model yields biased estimates. In this paper, the authors illustrate and discuss the key issues pertaining to inverse intensity rate ratio (IIRR) - weighted generalized estimating equations (GEE) methods in the context of a study of Kenyan mothers infected with human immunodeficiency virus type 1 (1999-2005). The authors estimated prevalences and prevalence ratios for morbid conditions affecting the women during a 1-year postpartum follow-up period. Of the 484 women under study, 62% had at least 1 as-needed visit. Use of a standard GEE model including both scheduled and unscheduled visits predicted a pneumonia prevalence of 2.9% (95% confidence interval : 2.3%, 3.5%), while use of the IIRR-weighted GEE predicted a prevalence of 1.5% (95% confidence interval : 1.2%, 1.8%). The estimate obtained using the IIRR-weighted GEE approach was compatible with estimates derived using scheduled visits only. These results highlight the importance of properly accounting for informative follow-up in these studies.
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