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 …

Hidden Markov models—most often abbreviated to the acronym “HMMs”—are one of the
most successful statistical modelling ideas that have came up in the last forty years: the use …

The idea for this book came from the time the authors spent at the Statistics and Applied
Mathematical Sciences Institute (SAMSI) in Research Triangle Park in North Carolina …

Missing Data in Clinical Studies provides a comprehensive account of the problems arising
when data from clinical and related studies are incomplete, and presents the reader with …

Missing data affect nearly every discipline by complicating the statistical analysis of collected
data. But since the 1990s, there have been important developments in the statistical …

Using standard missing data taxonomy, due to Rubin and co‐workers, and simple algebraic
derivations, it is argued that some simple but commonly used methods to handle incomplete …

In recent years, the use of the last observation carried forward (LOCF) approach in imputing
missing data in clinical trials has been greatly criticized, and several likelihood-based …

The past decade has seen a noticeable shift in missing data handling techniques that
assume a missing at random (MAR) mechanism, where the propensity for missing data on …

Repeated measures of various biomarkers provide opportunities for us to enhance
understanding of many important clinical aspects of CKD, including patterns of disease …

Background Chronic kidney disease (CKD) is a progressive and usually irreversible
disease. Different types of outcomes are of interest in the course of CKD such as time-to …