… In this chapter we give a broad introduction to the problem of missing data. We provide a …
process paying special attention to the setting of clinical studies (Section 1.2). We examine the …

… studies with missing data are discussed. Particularly, we focus on missing response and/or
covariate data for studies … models, models for longitudinal data such as generalized linear …

… Missing data constitute a problem present in all studies of medical research. The most …
handling missing data—complete case analysis—relies on assumptions about missing data that …

… The method for handling dropouts in clinical studies will often depend on the objective of
the study. On the one hand, the goal may be more explanatory in nature, as in the case of a …

… To evaluate the reporting and handling of missing data in clinical studies, we reviewed the
articles published in the October 2010 issues of three major critical care journals, Intensive …

… As no single analysis is definitive when missing data occurs, strategies for its … missing data
influences results and an understanding of the need for careful consideration of missing data …

… Thus, the MMRM method may be selected as the primary analysis method for clinical studies,
with other methods serving as sensitivity analyses to explore other dropout mechanisms. …

… was on missing data post dropout rather than missing interim data. Of all the papers in the
study, 9 (9%) had no missing data. … In clinical studies in general, missing data is a problem that …

… from clinical trials can be substantially reduced by missing data. … sizes or simplistic methods
for handling missing data, including last-… approach to addressing missing data is to prevent it. …

… of missing data (missing data mechanisms) that are important to determine the next steps
in missing data handling. The three missing data mechanisms are missing completely at …