Propensity score analyses (PSA) of continuous treatments often operationalize the treatment
as a multi-indicator composite, and its composite reliability is unreported. Latent variables or …

Although educational research and evaluation generally occur in multilevel settings, many
analyses ignore cluster effects. Neglecting the nature of data from educational settings …

Introduction Machine learning (ML) methods are promising and scalable alternatives for
propensity score (PS) estimation, but their comparative performance in disease risk score …

Background Estimating the average effect of a treatment, exposure, or intervention on health
outcomes is a primary aim of many medical studies. However, unbalanced covariates …

Purpose Few studies have measured the association between pre-existing comorbidities
and post-sepsis physical impairment. The study aimed to estimate the risk of physical …

Modification indices, common in structural equation modeling, are not applicable to latent
class analysis due to differences in estimation and fit assessment. To address this, we …

Aim Within the context of increasing mental distress noted since the beginning of the
COVID19 pandemic, the study aims at analysing the relationship between mental health …

Time series forecasting constitutes a cornerstone in decision-making processes across
diverse domains, ranging from finance and economics to healthcare and environmental …

Background Observational studies serve as an alternative to randomized experiments, but
they can introduce selection bias. Propensity score (PS) methods address this issue by …

Abstract Machine learning has become a common approach for estimating propensity
scores for quasi-experimental research using matching, weighting, or stratification on the …