TY - JOUR
AU - Kovalchik, Stephanie
AU - Varadhan, Ravi
PY - 2013/09/03
Y2 - 2023/03/28
TI - Fitting Additive Binomial Regression Models with the R Package blm
JF - Journal of Statistical Software
JA - J. Stat. Soft.
VL - 54
IS - 1
SE - Articles
DO - 10.18637/jss.v054.i01
UR - https://www.jstatsoft.org/index.php/jss/article/view/v054i01
SP - 1 - 18
AB - 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.
ER -