Treatment specific survival curves are an important tool to illustrate the treatment effect in
studies with time‐to‐event outcomes. In non‐randomized studies, unadjusted estimates can …

We used Monte Carlo simulations to compare the performance of asymptotic variance
estimators to that of the bootstrap when estimating standard errors of differences in means …

The inverse probability of treatment weighting (IPTW) approach is popular for evaluating
causal effects in observational studies, but extreme propensity scores could bias the …

Many causal processes have spatial and temporal dimensions. Yet the classic causal
inference framework is not directly applicable when the treatment and outcome variables are …

Background: Treatment of severe inpatient hypertension (HTN) that develops during
hospitalization is not informed by guidelines. Intravenous (iv) antihypertensives are used to …

Integrating multiple observational studies to make unconfounded causal or descriptive
comparisons of group potential outcomes in a large natural population is challenging …

When drawing causal inferences about the effects of multiple treatments on clustered
survival outcomes using observational data, we need to address implications of the …

Evaluating causal effects of an intervention in pre-specified subgroups is a standard goal in
comparative effectiveness research. Despite recent advancements in causal subgroup …

For time-to-event outcomes, the difference in restricted mean survival time is a measure of
the intervention effect, an alternative to the hazard ratio, corresponding to the expected …

Randomized controlled trials (RCTs) are the gold standard for causal inference but may lack
power because of small populations in rare diseases and limited participation in common …