Missing data are ubiquitous in medical research, yet there is still uncertainty over when
restricting to the complete records is likely to be acceptable, when more complex methods …

The year 2022 is the 20th anniversary of Joseph Schafer and John Graham's paper titled
“Missing data: Our view of the state of the art,” currently the most highly cited paper in the …

We propose a novel method for imputing missing data by adapting the well-known
Generative Adversarial Nets (GAN) framework. Accordingly, we call our method Generative …

Simulation studies are computer experiments that involve creating data by pseudo‐random
sampling. A key strength of simulation studies is the ability to understand the behavior of …

Background The rising number of young people going to university has led to concerns
about an increasing demand for student mental health services. We aimed to assess …

Longitudinal Structural Equation Modeling is a comprehensive resource that reviews
structural equation modeling (SEM) strategies for longitudinal data to help readers …

The last 20 years has seen an uptick in research on missing data problems, and most
software applications now implement one or more sophisticated missing data handling …

Multiple Imputation and its Application The most up-to-date edition of a bestselling guide to
analyzing partially observed data In this comprehensively revised Second Edition of Multiple …

Multiple imputation by chained equations is a flexible and practical approach to handling
missing data. We describe the principles of the method and show how to impute categorical …

The most user-friendly and authoritative resource on missing data has been completely
revised to make room for the latest developments that make handling missing data more …