Structural equation modeling (SEM) encompasses such diverse statistical techniques as path analysis, confirmatory factor analysis, causal modeling with latent variables, and even analysis of variance ...

Meta-Analytic Structural Equation Modeling (MASEM) represents a powerful and integrative statistical framework that combines the rigour of meta-analysis with the complexity of structural equation ...

Bayesian methods in Structural Equation Modeling (SEM) represent a paradigm shift in statistical analysis, integrating prior beliefs with empirical data to derive robust parameter estimates. This ...

Although the methodology for handling ordinal and dichotomous observed variables in structural equation models (SEMs) is developing rapidly, several important issues are unresolved. One of these is ...

The assessment of change in patient-reported outcomes is hindered by the fact that there are different types of change. Besides 'true' change, different types of response shift, such as recalibration, ...

R is a powerful open source programming environment primarily known for its statistical capabilities. In this course we will cover some advanced applications of R: distributed computing using the ...

Defining undertreatment and overtreatment in older patients with cancer: A scoping review of the literature. This is an ASCO Meeting Abstract from the 2019 ASCO Annual Meeting I. This abstract does ...