Analyzing hospitalization data: potential limitations of Poisson regression | Zendy

Colin Weaver | Zendy; Pietro Ravani | Zendy; Matthew J. Oliver | Zendy; Peter C. Austin | Zendy; Robert R. Quinn | Zendy

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Analyzing hospitalization data: potential limitations of Poisson regression

Author(s) -

Colin Weaver,

Pietro Ravani,

Matthew J. Oliver,

Peter C. Austin,

Robert R. Quinn

Publication year - 2015

Publication title -

nephrology dialysis transplantation

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 1.654

H-Index - 168

eISSN - 1460-2385

pISSN - 0931-0509

DOI - 10.1093/ndt/gfv071

Subject(s) - poisson regression , overdispersion , negative binomial distribution , statistics , poisson distribution , confidence interval , medicine , count data , regression analysis , quasi likelihood , regression , linear regression , relative risk , proportional hazards model , mathematics , population , environmental health

Poisson regression is commonly used to analyze hospitalization data when outcomes are expressed as counts (e.g. number of days in hospital). However, data often violate the assumptions on which Poisson regression is based. More appropriate extensions of this model, while available, are rarely used.

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