Logistic regression was preferred to estimate risk differences and numbers needed to be exposed adjusted for covariates
Accepted 22 January 2010. published online 30 April 2010. Corrected Proof
Abstract
Objective
The estimation of the number needed to be exposed (NNE) with adjustment for covariates can be performed by inverting the corresponding adjusted risk difference. The latter can be estimated by several approaches based on binomial and Poisson regression with or without constraints. A novel proposal is given by logistic regression with average risk difference (LR-ARD) estimation. Finally, the use of ordinary linear regression and unadjusted estimation can be considered.
Study Design and Setting
LR-ARD is compared with alternative approaches regarding bias, precision, and coverage probability by means of an extensive simulation study.
Results
LR-ARD was found to be superior compared with the other approaches. In the case of balanced covariates and large sample sizes, unadjusted estimation and ordinary linear regression can also be used. In general, however, LR-ARD seems to be the most appropriate approach to estimate adjusted risk differences and NNEs.
Conclusions
To estimate risk differences and NNEs with adjustment for covariates, the LR-ARD approach should be used.
aDepartment of Medical Biometry, Institute for Quality and Efficiency in Health Care (IQWiG), Cologne, Germany
bInstitute for Medical Epidemiology, Biostatistics, and Informatics (IMEBI), Martin-Luther-University Halle-Wittenberg, Halle (Saale), Germany
cInstitute of Epidemiology and Social Medicine, University of Münster, Münster, Germany
dFaculty of Medicine, University of Cologne, Cologne, Germany
Corresponding author. Department of Medical Biometry, Institute for Quality and Efficiency in Health Care (IQWiG), Dillenburger Street 27, D-51105 Cologne, Germany. Tel.: +49-221-35685-451; fax: +49-221-35685-891.
ABSTRACT