A comparison of full model specification and backward elimination of potential confounders when estimating marginal and conditional causal effects on binary outcomes from observational data

Author:

Luijken Kim1,Groenwold Rolf H.H.12,van Smeden Maarten13,Strohmaier Susanne45,Heinze Georg4

Affiliation:

1. Department of Clinical Epidemiology Leiden University Medical Center Leiden The Netherlands

2. Department of Biomedical Data Sciences Leiden University Medical Center Leiden The Netherlands

3. Julius Center for Health Sciences and Primary Care University Medical Center Utrecht University of Utrecht Utrecht The Netherlands

4. Section for Clinical Biometrics Center for Medical Statistics Informatics and Intelligent Systems Medical University of Vienna Vienna Austria

5. Department of Epidemiology Center for Public Health Medical University of Vienna Vienna Austria

Funder

ZonMw

Horizon 2020 Framework Programme

Austrian Science Fund

Publisher

Wiley

Subject

Statistics, Probability and Uncertainty,General Medicine,Statistics and Probability

Reference49 articles.

1. Reporting of covariate selection and balance assessment in propensity score analysis is suboptimal: A systematic review;Ali M. S.;Journal of Clinical Epidemiology,2015

2. Absolute risk reductions, relative risks, relative risk reductions, and numbers needed to treat can be obtained from a logistic regression model;Austin P. C.;Journal of Clinical Epidemiology,2010

3. Post‐selection inference for generalized linear models with many controls;Belloni A.;Journal of Business & Economic Statistics,2016

4. Valid post‐selection inference;Berk R.;The Annals of Statistics,2013

5. Bias correction in generalized linear models;Cordeiro G. M.;Journal of the Royal Statistical Society: Series B (Methodological),1991

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