BackgroundConfounding bias is a common concern in epidemiological research. Its presence is often determined by comparing exposure effects between univariable- and multivariable regression models, using an arbitrary threshold of a 10% difference to indicate confounding bias. However, many clinical researchers are not aware that the use of this change-in-estimate criterion may lead to wrong conclusions when applied to logistic regression coefficients. This is due to a statistical phenomenon called noncollapsibility, which manifests itself in logistic regression models. This paper aims to clarify the role of noncollapsibility in logistic regression and to provide guidance in determining the presence of confounding bias.MethodsA Monte Carlo simulation study was designed to uncover patterns of confounding bias and noncollapsibility effects in logistic regression. An empirical data example was used to illustrate the inability of the change-in-estimate criterion to distinguish confounding bias from noncollapsibility effects.ResultsThe simulation study showed that, depending on the sign and magnitude of the confounding bias and the noncollapsibility effect, the difference between the effect estimates from univariable- and multivariable regression models may underestimate or overestimate the magnitude of the confounding bias. Because of the noncollapsibility effect, multivariable regression analysis and inverse probability weighting provided different but valid estimates of the confounder-adjusted exposure effect. In our data example, confounding bias was underestimated by the change in estimate due to the presence of a noncollapsibility effect.ConclusionIn logistic regression, the difference between the univariable- and multivariable effect estimate might not only reflect confounding bias but also a noncollapsibility effect. Ideally, the set of confounders is determined at the study design phase and based on subject matter knowledge. To quantify confounding bias, one could compare the unadjusted exposure effect estimate and the estimate from an inverse probability weighted model.
MULTIFILE
This paper evaluates the impact of an online game-based financial education tool on students' financial literacy levels. By conducting a Randomized Controlled Trial (RCT) involving 2,220 students across four countries in a multi-country experimental setting, we demonstrate that the intervention significantly enhances students' financial literacy levels by 0.313 SD. This study contributes to the emerging academic literature concerning the evaluation of financial education interventions that incorporate learning-by-playing. The participation of students from four countries adds relevance by facilitating cross-comparison of outcomes and stimulating discussions about country-specific factors and peculiarities influencing youth financial literacy.
DOCUMENT
Circulating sex hormone levels have been linked to a wide range of cardiovascular risk factors in men, but studies on incident CVD have been inconclusive [1]. Recent data from meta-analyses show an increase in CVD risk with low testosterone in elderly men and no association with estradiol levels [2,3]. To clarify the role of sex hormones in male CVD risk, we examined the cross-sectional and longitudinal associations between endogenous sex hormones and subclinical atherosclerosis in a population-based cohort of middle-aged and older men.
DOCUMENT
