Related Experiment Video
Updated: Jul 11, 2026

Tactile Semiautomatic Passive-Finger Angle Stimulator (TSPAS)
Published on: July 30, 2020
Estimating adjusted NNT measures in logistic regression analysis
Ralf Bender1, Oliver Kuss, Mandy Hildebrandt
1Department of Medical Biometry, Institute for Quality and Efficiency in Health Care (IQWiG), Cologne, Germany. Ralf.Bender@iqwig.de Ralf@rbsd.de
Abstract:
The number needed to treat (NNT) is a popular measure to describe the absolute effect of a new treatment compared with a standard treatment or placebo in clinical trials with binary outcome. For use of NNT measures in epidemiology to compare exposed and unexposed subjects, the terms 'number needed to be exposed' (NNE) and 'exposure impact number' (EIN) have been proposed. Additionally, in the framework of logistic regression a method was derived to perform point and interval estimation of NNT measures with adjustment for confounding by using the adjusted odds ratio (OR approach). In this paper, a new method is proposed which is based upon the average risk difference over the observed confounder values (ARD approach). A decision has to be made, whether the effect of allocating an exposure to unexposed persons or the effect of removing an exposure from exposed persons should be described. We use the term NNE for the first and the term EIN for the second situation. NNE is the average number of unexposed persons needed to be exposed to observe one extra case; EIN is the average number of exposed persons among one case can be attributed to the exposure. By means of simulations it is shown that the ARD approach is better than the OR approach in terms of bias and coverage probability, especially if the confounder distribution is wide. The proposed method is illustrated by application to data of a cohort study investigating the effect of smoking on coronary heart disease.
Related Concept Videos
The Mantel-Cox Log-Rank Test
Regression Toward the Mean
Assumptions of Survival Analysis
Truncation in Survival Analysis
Left truncation occurs when individuals who experienced the event of interest before a certain time are not included in the study. This is often due to a "delayed entry" into the study where only those who survive until a certain entry point are observed.
Residuals and Least-Squares Property
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
Survival Tree
Building a Survival Tree
Constructing a survival tree begins...