Logistic regression was preferred to estimate risk differences and numbers needed to be exposed adjusted for covariates.

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.