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Longitudinal Data Analysis for Generalized Linear Models Under Participant-Driven Informative Follow-up: An Application in Maternal Health Epidemiology.
- Source :
- American Journal of Epidemiology; Jan2010, Vol. 171 Issue 2, p189-197, 9p, 3 Charts, 1 Graph
- Publication Year :
- 2010
-
Abstract
- 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. [ABSTRACT FROM PUBLISHER]
Details
- Language :
- English
- ISSN :
- 00029262
- Volume :
- 171
- Issue :
- 2
- Database :
- Complementary Index
- Journal :
- American Journal of Epidemiology
- Publication Type :
- Academic Journal
- Accession number :
- 51492130
- Full Text :
- https://doi.org/10.1093/aje/kwp353