Researchers have long struggled to identify causal effects in nonexperimental settings.
Many recently proposed strategies assume ignorability of the treatment assignment …

Propensity and prognostic score methods seek to improve the quality of causal inference in
non‐randomized or observational studies by replicating the conditions found in a controlled …

Observational studies compare subjects who received a specified treatment to others who
did not, without controlling assignment to treatment and comparison groups. When the …

A recent systematic review of experimental studies conducted in Turkey between 2010 and
2020 reported that small sample sizes had been a significant drawback (Bulus & Koyuncu …

Propensity score analysis is a common approach to addressing confounding in
nonrandomized studies. Its implementation, however, requires important assumptions (eg …

Observational studies often benefit from an abundance of observational units. This can lead
to studies that—while challenged by issues of internal validity—have inferences derived …

Unlike in randomized clinical trials (RCTs), confounding control is critical for estimating the
causal effects from observational studies due to the lack of treatment randomization. Under …

Background Multivariable confounder adjustment in comparative studies of newly marketed
drugs can be limited by small numbers of exposed patients and even fewer outcomes …

Since the introduction of the propensity score (PS), methods for estimating treatment effects
with observational data have received growing attention in the literature. Recent research …

A propensity score (PS) model's ability to control confounding can be assessed by
evaluating covariate balance across exposure groups after PS adjustment. The optimal …