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Fitting Additive Binomial Regression Models with the R Package blm

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  • Kovalchik, Stephanie
  • Varadhan, Ravi

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.

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  • Kovalchik, Stephanie & Varadhan, Ravi, 2013. "Fitting Additive Binomial Regression Models with the R Package blm," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 54(i01).
    • Handle: RePEc:jss:jstsof:v:054:i01
    DOI: http://hdl.handle.net/10.18637/jss.v054.i01
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    1. Donald W. K. Andrews, 2000. "Inconsistency of the Bootstrap when a Parameter Is on the Boundary of the Parameter Space," Econometrica, Econometric Society, vol. 68(2), pages 399-406, March.
    2. Archer, Kellie J. & Lemeshow, Stanley & Hosmer, David W., 2007. "Goodness-of-fit tests for logistic regression models when data are collected using a complex sampling design," Computational Statistics & Data Analysis, Elsevier, vol. 51(9), pages 4450-4464, May.
    3. Barry I. Graubard & Thomas R. Fears, 2005. "Standard Errors for Attributable Risk for Simple and Complex Sample Designs," Biometrics, The International Biometric Society, vol. 61(3), pages 847-855, September.
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    1. Daniel D. Reidpath & Ireneous Soyiri & Nowrozy K. Jahan & Devi Mohan & Badariah Ahmad & Mohtar Pungut Ahmad & Zaid Bin Kassim & Pascale Allotey, 2018. "Poor glycaemic control and its metabolic and demographic risk factors in a Malaysian community-based study," International Journal of Public Health, Springer;Swiss School of Public Health (SSPH+), vol. 63(2), pages 193-202, March.

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