Published by the Foundation for Open Access Statistics Editors-in-chief: Bettina Grün, Torsten Hothorn, Rebecca Killick, Edzer Pebesma, Achim Zeileis    ISSN 1548-7660; CODEN JSSOBK
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Authors: Stephanie Kovalchik, Ravi Varadhan
Title: Fitting Additive Binomial Regression Models with the R Package blm
Abstract: The R package blm provides functions for fitting a family of additive regression models to binary data. The included models are the binomial linear model, in which all covariates have additive effects, and the linear-expit (lexpit) model, which allows some covariates to have additive effects and other covariates to have logisitc effects. Additive binomial regression is a model of event probability, and the coefficients of linear terms estimate covariate-adjusted risk differences. Thus, in contrast to logistic regression, additive binomial regression puts focus on absolute risk and risk differences. In this paper, we give an overview of the methodology we have developed to fit the binomial linear and lexpit models to binary outcomes from cohort and population-based case-control studies. We illustrate the blm package’s methods for additive model estimation, diagnostics, and inference with risk association analyses of a bladder cancer nested case-control study in the NIH-AARP Diet and Health Study.

Page views:: 4173. Submitted: 2011-11-22. Published: 2013-09-03.
Paper: Fitting Additive Binomial Regression Models with the R Package blm     Download PDF (Downloads: 5938)
blm_2013.2.4.4.tar.gz: R source package Download (Downloads: 331; 33KB)
v54i01.R: R example code from the paper Download (Downloads: 418; 1KB)

DOI: 10.18637/jss.v054.i01

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Paper: Creative Commons Attribution 3.0 Unported License
Code: GNU General Public License (at least one of version 2 or version 3) or a GPL-compatible license.