ArticleEpidemiology (Cambridge, Mass.)2026
An Improved Pooled Logistic Regression Implementation.
Article in Epidemiology (Cambridge, Mass.), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
2 citing papers in PubMed.
- A Flexible-Horizon Clinical Decision Support Model for Dementia Early Detection and Risk Prediction in Secondary Care: A Responsible AI Approach.Journal of medical systems · 2026Article
- Pooled Multinomial Logistic Regression for Parametric G-computation in the Presence of Competing Events.Epidemiology (Cambridge, Mass.) · 2026Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
backgroundPooled logistic regression is a popular tool for survival analyses in epidemiology, but can face computational challenges. Commonly, these challenges are addressed through widening time intervals or using a parametric functional form for time. We propose a third option to reduce the computational burden without constraining the functional form for time.
methodsThe proposed algorithm operates by restricting the long data set to rows that correspond to unique event times. However, our approach is only compatible when modeling time most flexibly with disjoint indicators. We compared the standard implementation to the proposed implementation in SAS, R, and Python using a publicly available data set.
resultsFor the example considered, both implementations provided the same point estimates, but the proposed implementation was between 6 and 68 times faster depending on the software.
conclusionsThe proposed implementation can greatly simplify estimation of pooled logistic regression models, which is especially important when relying on the bootstrap for inference.
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Registered trials
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