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 …

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 …

I review assumptions about the missing-data mechanisms that underlie methods for the
statistical analysis of data with missing values. I describe Rubin's original definition of …

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 …

Missing data are a pervasive problem in data analysis. Three common methods for
addressing the problem are (a) complete-case analysis, where only units that are complete …

Missing data are a commonly occurring threat to the validity and efficiency of epidemiologic
studies. Perhaps the most common approach to handling missing data is to simply drop …

Although recent work has highlighted the relation of acculturative stress with depression and
anxiety symptoms specifically among Latino/a university students, the potential mechanisms …

The development of coherent missing data models to account for nonmonotone missing at
random (MAR) data by inverse probability weighting (IPW) remains to date largely …

Missing values in covariates of regression models are a pervasive problem in empirical
research. Popular approaches for analyzing partially observed datasets include complete …

We present a systematic approach to the practical and comprehensive handling of missing
data motivated by our experiences of analyzing longitudinal survey data. We consider the …