Characterization of circulating leukocytes and correlation of leukocyte subsets with metabolic parameters 1 and 5 years after diabetes diagnosis.

Aims: Infiltration of pancreatic islets with different leukocyte subtypes likely contributes to deterioration of glycemia in diabetes mellitus. Different subsets of leukocytes have been previously associated with type 1 or type 2 diabetes. This study aimed at examining these subsets at different stages of diabetes progression and possible relationships with metabolic parameters.

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.

Estimating adjusted NNT measures in logistic regression analysis.

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).

Calculating confidence intervals for impact numbers.

Background: Standard effect measures such as risk difference and attributable risk are frequently used in epidemiological studies and public health research to describe the effect of exposures. Recently, so-called impact numbers have been proposed, which express the population impact of exposures in form of specific person or case numbers. To describe estimation uncertainty, it is necessary to calculate confidence intervals for these new effect measures.