Meta-analyses contribute critically to cumulative science, but they can produce misleading
conclusions if their constituent primary studies are biased, for example by unmeasured …

Confounding can cause substantial bias in nonexperimental studies that aim to estimate
causal effects. Propensity score methods allow researchers to reduce bias from measured …

Numerous observational studies have attempted to identify risk factors for infection with
SARS-CoV-2 and COVID-19 disease outcomes. Studies have used datasets sampled from …

Background: Obesity is a risk factor for pneumonia and acute respiratory distress syndrome.
Objective: To determine whether obesity is associated with intubation or death …

Outcome-Wide Longitudinal Designs for Causal Inference: A New Template for Empirical
Studies Page 1 Statistical Science 2020, Vol. 35, No. 3, 437–466 https://doi.org/10.1214/19-STS728 …

Background Studies of preterm delivery after COVID-19 are often subject to selection bias
and do not distinguish between early vs. late infection in pregnancy, nor between …

Response to survey questionnaires is vital for social and behavioural research, and most
analyses assume full and accurate response by participants. However, nonresponse is …

Background: Tea is frequently consumed worldwide, but the association of tea drinking with
mortality risk remains inconclusive in populations where black tea is the main type …

Large studies such as UK Biobank are increasingly used for GWAS and Mendelian
randomization (MR) studies. However, selection into and dropout from studies may bias …

Introduction Understanding the population impact of e-cigarettes requires determining their
effect on cigarette smoking cessation. Methods Using the US Population Assessment of …