We used a Bayesian hierarchical selection model to study publication bias in 1106 meta‐
analyses from the Cochrane Database of Systematic Reviews comparing treatment with …

An up-to-date, comprehensive treatment of a classic text on missing data in statistics The
topic of missing data has gained considerable attention in recent decades. This new edition …

Data Analysis Using Regression and Multilevel/Hierarchical Models is a comprehensive
manual for the applied researcher who wants to perform data analysis using linear and …

It's been over a decade since the first edition of Measurement Error in Nonlinear Models
splashed onto the scene, and research in the field has certainly not cooled in the interim. In …

The distributions of parasitic diseases are determined by complex factors, including many
that are distributed in space. A variety of statistical methods are now readily accessible to …

The growth of biostatistics has been phenomenal in recent years and has been marked by
considerable technical innovation in both methodology and computational practicality. One …

Spatial epidemiology is the description and analysis of the geographical distribution of
disease. It is more important now than ever, with modern threats such as bio-terrorism …

Drawing from the authors' own work and from the most recent developments in the field,
Missing Data in Longitudinal Studies: Strategies for Bayesian Modeling and Sensitivity …

BACKGROUND: The traditional paradigm of glomerular filtration rate (GFR) progression in
patients with chronic kidney disease (CKD) is a steady nearly linear decline over time. We …

Generalized linear mixed models (GLMMs) continue to grow in popularity due to their ability
to directly acknowledge multiple levels of dependency and model different data types. For …