Epidemiologic studies are frequently susceptible to missing information. Omitting
observations with missing variables remains a common strategy in epidemiologic studies …

Background Multiple imputation has become very popular as a general-purpose method for
handling missing data. The validity of multiple-imputation-based analyses relies on the use …

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 …

Although modern lines for dealing with missing data are well established from the 1970s,
today there is a challenge when researchers encounter this problem in multilevel models …

Multiple imputation is gaining popularity as a strategy for handling missing data, but there is
a scarcity of tools for checking imputation models, a critical step in model fitting. Posterior …

Conventional multiple imputation (MI) replaces the missing values in a dataset by m> 1 sets
of simulated values. I explore a two-stage extension of MI in which the missing data are …

Multiple imputation by chained equations (MICE) is the most common method for imputing
missing data. In the MICE algorithm, imputation can be performed using a variety of …

In many modeling contexts, the variables in the model are linear composites of the raw items
measured for each participant; for instance, regression and path analysis models rely on …

Multiple imputation (MI) has emerged in the last two decades as a frequently used approach
in dealing with incomplete data. Gaussian and log‐linear imputation models are fairly …