Academic Journal
Inverse-Intensity-Weighted Generalized Estimating Equations With Irregularly Measured Longitudinal Data and Informative Dropout.
| Τίτλος: | Inverse-Intensity-Weighted Generalized Estimating Equations With Irregularly Measured Longitudinal Data and Informative Dropout. |
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| Συγγραφείς: | Stefan G; Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada.; Child Health Evaluative Sciences, The Hospital for Sick Children, Toronto, Ontario, Canada., Pullenayegum E; Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada.; Child Health Evaluative Sciences, The Hospital for Sick Children, Toronto, Ontario, Canada. |
| Πηγή: | Statistics in medicine [Stat Med] 2026 Aug; Vol. 45 (18-19), pp. e70674. |
| Τύπος έκδοσης: | Journal Article |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: Wiley Country of Publication: England NLM ID: 8215016 Publication Model: Print Cited Medium: Internet ISSN: 1097-0258 (Electronic) Linking ISSN: 02776715 NLM ISO Abbreviation: Stat Med Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: Chichester ; New York : Wiley, c1982- |
| Ιατρικοί όροι (MeSH): | Patient Dropouts*/statistics & numerical data , Models, Statistical*, Humans ; Longitudinal Studies ; Computer Simulation ; Bias ; Randomized Controlled Trials as Topic ; Data Interpretation, Statistical |
| Περίληψη: | Longitudinal data are commonly encountered in biomedical research, including randomized trials and retrospective cohort studies. Subjects are typically followed over a period of time and may be scheduled for follow-up at predetermined time points. However, subjects may miss their appointments or return at non-specified times, leading to irregularity in the visit process. IIW-GEEs have been developed as one method to account for this irregularity, whereby estimates from a visit intensity model are used as weights in a GEE model with an independent correlation structure. We show that currently available methods can be biased for situations in which the health outcome of interest may influence a subject's dropout from the study. We have extended the IIW-GEE framework to adjust for informative dropout and have demonstrated via simulation studies that this bias can be significantly reduced. We have illustrated this method using the STAR*D clinical trial data, and observed that the disease trajectory was generally overestimated when informative dropout was not accounted for. (© 2026 The Author(s). Statistics in Medicine published by John Wiley & Sons Ltd.) |
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| Grant Information: | RGPIN-2021-02733 Natural Sciences and Engineering Research Council of Canada |
| Contributed Indexing: | Keywords: GEE; informative dropout; inverse‐intensity weighting; irregular observation; longitudinal data |
| Entry Date(s): | Date Created: 20260721 Date Completed: 20260721 Latest Revision: 20260813 |
| Update Code: | 20260814 |
| PubMed Central ID: | PMC13387368 |
| DOI: | 10.1002/sim.70674 |
| PMID: | 42479297 |
| Βάση Δεδομένων: | MEDLINE |
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