Abstract Machine learning is increasingly used in mental health research and has the
potential to advance our understanding of how to characterize, predict, and treat mental …

Abstract Background Directed acyclic graphs (DAGs) are an increasingly popular approach
for identifying confounding variables that require conditioning when estimating causal …

Introduction In 2020, prior to COVID-19 vaccine rollout, the Brighton Collaboration created a
priority list, endorsed by the World Health Organization, of potential adverse events relevant …

Accurate estimates of the burden of SARS-CoV-2 infection are critical to informing pandemic
response. Confirmed COVID-19 case counts in the US do not capture the total burden of the …

Despite broad agreement on the negative consequences of vaccine inequity, the distribution
of COVID-19 vaccines is imbalanced. Access to vaccines in high-income countries (HICs) is …

Background Opioid agonist therapy is strongly recommended for pregnant persons with
opioid use disorder. Buprenorphine may be associated with more favorable neonatal and …

Selecting an appropriate set of confounders for which to control is critical for reliable causal
inference. Recent theoretical and methodological developments have helped clarify a …

Sensitivity analysis is useful in assessing how robust an association is to potential
unmeasured or uncontrolled confounding. This article introduces a new measure called the …

Randomized trials serve as the standard for comparative studies of treatment effects. In
many settings, it may not be feasible or ethical to conduct a randomized study, 1 and …

Background In 2013, New York began requiring hospitals to follow protocols for the early
identification and treatment of sepsis. However, there is controversy about whether more …