Efficiency Lost By Analyzing Counts Rather Than Event Times in Poisson and Overdispersed Poisson Regression Models

DOI10.2307/2965408zbMath0913.62079OpenAlexW4230910070MaRDI QIDQ4376011

C. B. Dean, Robert F. Balshaw

Publication date: 10 June 1999

Full work available at URL: https://doi.org/10.2307/2965408

zbMATH Keywords

clinical trialquasi-likelihoodunobserved heterogeneityestimating equationrandom effectnegative binomial regressionrecurrent event

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Non-Markovian processes: estimation (62M09) Markov processes: estimation; hidden Markov models (62M05)

Related Items (11)

Portmanteau test for randomness in poisson data ⋮ Regression splines in the quasi-likelihood analysis of recurrent event data ⋮ Modelling and analysis of count data using a renewal process ⋮ Semi-supervised approach to event time annotation using longitudinal electronic health records ⋮ Generalized linear mixed models: a review and some extensions ⋮ Spatial logistic regression and change-of-support in Poisson point processes ⋮ On familial longitudinal Poisson mixed models with gamma random effects. ⋮ Robust Tests for Treatment Effects Based on Censored Recurrent Event Data Observed over Multiple Periods ⋮ Model misspecification effects in clustered count data analysis ⋮ A Semiparametric Model for the Analysis of Recurrent-Event Panel Data ⋮ Comparison of the Andersen-Gill model with Poisson and negative binomial regression on recurrent event data

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