Stable computational methods for additive binomial models with application to adjusted risk differences

DOI10.1016/J.CSDA.2014.06.019zbMath1506.62055OpenAlexW2047096297MaRDI QIDQ1623708

Ian C. Marschner, Mark W. Donoghoe

Publication date: 23 November 2018

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: http://hdl.handle.net/2123/17611

zbMATH Keywords

semiparametric regressionrisk differencemultinomial-Poisson transformationadditive binomial model

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Applications of statistics to biology and medical sciences; meta analysis (62P10) Generalized linear models (logistic models) (62J12)

Uses Software

• R
• SAS
• SAS/STAT
• glm2
• GLMCURV9

Cites Work

• GLMCURV9
• Combinatorial EM algorithms
• Relative risk regression: reliable and flexible methods for log-binomial models
• Estimation of a Common Effect Parameter from Sparse Follow-Up Data
• Estimating Risk Difference in Multicenter Studies Under Baseline‐Risk Heterogeneity
• Some Methods for Strengthening the Common χ 2 Tests
• Unnamed Item

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