title: Regression standardization with the R package stdReg.
abstract: When studying the association between an exposure and an outcome, it is common to use regression models to adjust for measured confounders. The most common models in epidemiologic research are logistic regression and Cox regression, which estimate conditional (on the confounders) odds ratios and hazard ratios. When the model has been fitted, one can use regression standardization to estimate marginal measures of association. If the measured confounders are sufficient for confounding control, then the marginal association measures can be interpreted as poulation causal effects. In this paper we describe a new R package, stdReg, that carries out regression standardization with generalized linear models (e.g. logistic regression) and Cox regression models. We illustrate the package with several examples, using real data that are publicly available.
authors.affiliation: , Nobels väg 12 A, 171 77, Stockholm, Sweden. arvid.sjolander@ki.se.
authors.forename: Arvid
authors.identifier: 0000-0001-5226-6685
authors.initials: A
authors.lastname: Sjölander
mesh_terms: D016022:Case-Control Studies; D015984:Causality; D016021:Epidemiologic Studies; D006801:Humans; D016014:Linear Models; D016015:Logistic Models; D016017:Odds Ratio; D016016:Proportional Hazards Models; D012015:Reference Standards
keywords: Cox regression; Hazard ratio; Logistic regression; Odds ratio; Standardization