Since their mainstream introduction in the 1990s, causal diagrams, including directed acyclic graphs (DAGs), have been increasingly used to depict our caus

Since their mainstream introduction in the 1990s, causal diagrams, including directed acyclic graphs (DAGs), have been increasingly used to depict our caus

A fresh, charming, socially conscious tour of the mysteries of space-time, from the award-winning author of The Disordered Cosmos In her highly acclaimed debut, distinguished cosmologist and particle...

Olivia Guest & Iris van Rooij urge teachers and scholars to reject tools that commodify learning, deskill students, and promote illiteracy.

Two institutions are coming together to create an alternative to the CDC’s vaunted Morbidity and Mortality Weekly Report

UCLA’s three most recent alumni Nobel winners — Fred Ramsdell, Ardem Patapoutian and Randy Schekman — said their experience as Bruins loomed particularly large in their scientific development.

These discoveries span decades and disciplines, but they all have one important thing in common: They’ve all relied on competitive funding from the federal government.

Epidemiological researchers often examine associations between risk factors and health outcomes in non-experimental designs. Observed associations may be causal or confounded by unmeasured factors. Sibling and co-twin control studies account for familial confounding by comparing exposure levels among siblings (or twins). If the exposure-outcome association is causal, the siblings should also differ regarding the outcome. However, such studies may sometimes introduce more bias than they alleviate. Measurement error in the exposure may bias results and lead to erroneous conclusions that truly causal exposure-outcome associations are confounded by familial factors. The current study used Monte Carlo simulations to examine bias due to measurement error in sibling control models when the observed exposure-outcome association is truly causal. The results showed that decreasing exposure reliability and increasing sibling-correlations in the exposure led to deflated exposure-outcome associations and inflated associations between the family mean of the exposure and the outcome. The risk of falsely concluding that causal associations were confounded was high in many situations. For example, when exposure reliability was 0.7 and the observed sibling-correlation was r = 0.4, about 30–90% of the samples (n = 2,000) provided results supporting a false conclusion of confounding, depending on how p-values were interpreted as evidence for a family effect on the outcome. The current results have practical importance for epidemiological researchers conducting or reviewing sibling and co-twin control studies and may improve our understanding of observed associations between risk factors and health outcomes. We have developed an app (SibSim) providing simulations of many situations not presented in this paper.

Water fluoridation has been deemed one of the top 10 public health successes of the twentieth century [1]. It is thought to have reduced caries incidence b

A recommendation by Western states allows anyone over 6 months old to get a COVID-19 shot. California insurance providers will have to cover the cost.