Longitudinal data analysis for generalized linear models under participant-driven informative follow-up: an application in maternal health epidemiology.
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.
Bůzková
Petra
P
Department of Biostatistics, School of Public Health, University of Washington, 6200 NE 74th Street, Seattle, WA 98115, USA. buzkova@u.washington.edu
Brown
Elizabeth R
ER
John-Stewart
Grace C
GC
D000328:Adult; D005260:Female; D015658:HIV Infections / Q000453:epidemiology* / Q000401:mortality; D015497:HIV-1; D006801:Humans; D007630:Kenya / Q000453:epidemiology; D016014:Linear Models; D008137:Longitudinal Studies; D008429:Maternal Welfare / Q000706:statistics & numerical data*; D015995:Prevalence