Comparison of the Andersen-Gill model with Poisson and negative binomial regression on recurrent event data
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Publication:1023865
DOI10.1016/j.csda.2008.04.009zbMath1452.62076OpenAlexW2003766005MaRDI QIDQ1023865
Publication date: 16 June 2009
Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.csda.2008.04.009
Computational methods for problems pertaining to statistics (62-08) Applications of statistics to biology and medical sciences; meta analysis (62P10) Estimation in survival analysis and censored data (62N02)
Related Items (3)
Functional modeling of recurrent events on time‐to‐event processes ⋮ Empirical assessment of the maximum likelihood estimator quality in a parametric counting process model for recurrent events ⋮ Sample-size calculation and reestimation for a semiparametric analysis of recurrent event data taking robust standard errors into account
Cites Work
- Cox's regression model for counting processes: A large sample study
- Modeling survival data: extending the Cox model
- The Robust Inference for the Cox Proportional Hazards Model
- Mixed Poisson Likelihood Regression Models for Longitudinal Interval Count Data
- On the regression analysis of multivariate failure time data
- Robust Tests for Treatment Comparisons Based on Recurrent Event Responses
- Efficiency Lost By Analyzing Counts Rather Than Event Times in Poisson and Overdispersed Poisson Regression Models
- Wei, Lin and Weissfeld's marginal analysis of multivariate failure time data
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