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June 22, 2018Open Access

Comparing performance between log-binomial and robust Poisson regression models for estimating risk ratios under model misspecification

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Abstract

Under model misspecification, the robust Poisson model was generally preferable because it provided unbiased estimates of risk ratios.

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Authors

Wansu Chen

Kaiser Permanente

Lei Qian

Kaiser Permanente

Jiaxiao Shi

Kaiser Permanente

Journals

SHILAP Revista de lepidopterología

BMC Medical Research Methodology

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Institutions

University of Southern California

Kaiser Permanente

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Chen et al. (Fri,) studied this question.

synapsesocial.com/papers/69d6c48da0177bf533ed8e81 — DOI: https://doi.org/10.1186/s12874-018-0519-5

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Quasi-likelihood functions, generalized linear models, and the Gauss—Newton method· 1974 · 1,954 citations
Foundations of Linear and Generalized Linear Models· 2015 · 473 citations

Also consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

Quasi-Likelihood Functions· 1983 · 776 citations
Asthma Quality-of-Care Markers Using Administrative Data· 2005 · 110 citations
The behavior of maximum likelihood estimates under nonstandard conditions· 1967 · 4,759 citations
Quasi-likelihood functions, generalized linear models, and the Gauss—Newton method· 1974 · 1,954 citations
Foundations of Linear and Generalized Linear Models· 2015 · 473 citations

Comparing performance between log-binomial and robust Poisson regression models for estimating risk ratios under model misspecification

Key Points

Abstract

Citation Network

Connected Papers

Discussion

Authors

Journals

Actions

Institutions

References and Citations

Citation Network

Connected Papers

Discussion

Cite this study

Also consider

Also consider