Missing data pose challenges to real-life data analysis. Simple ad-hoc fixes, like deletion or
mean imputation, only work under highly restrictive conditions, which are often not met in …

Clinical prediction models aim to provide estimates of absolute risk for a diagnostic or
prognostic endpoint. Such models may be derived from data from various studies in the …

Supplement to “Multiple Imputation for Multilevel Data with Continuous and Binary
Variables”. Technical details on the posterior distributions of imputation model parameters …

Purpose Pharmacoepidemiologic multi‐database studies (MDBS) provide opportunities to
better evaluate the safety and effectiveness of medicines. However, the issue of missing …

Applications of multiple imputation have long outgrown the traditional context of dealing with
item nonresponse in cross-sectional datasets. Nowadays multiple imputation is also applied …

Estimating relationships between multiple incomplete patient measurements requires
methods to cope with missing values. Multiple imputation is one approach to address …

Real-world data sources offer opportunities to compare the effectiveness of treatments in
practical clinical settings. However, relevant outcomes are often recorded selectively and …

Causally interpretable meta-analysis combines information from a collection of randomized
controlled trials to estimate treatment effects in a target population in which experimentation …

Background The causes of racial disparities in epithelial ovarian cancer (EOC) incidence
remain unclear. Differences in the prevalence of ovarian cancer risk factors may explain …

This book introduces best practices in longitudinal data analysis at intermediate level, with a
minimum number of formulas without sacrificing depths. It meets the need to understand …