Multiple Imputation and its Application (PDF)

A practical guide to analysing partially observed
data.

Collecting, analysing and drawing inferences from data is
central to research in the medical and social sciences.
Unfortunately, it is rarely possible to collect all the intended
data. The literature on inference from the resulting
incomplete data is now huge, and continues to grow both as
methods are developed for large and complex data structures, and as
increasing computer power and suitable software enable researchers
to apply these methods.

This book focuses on a particular statistical method for
analysing and drawing inferences from incomplete data, called
Multiple Imputation (MI). MI is attractive because it is both
practical and widely applicable. The authors aim is to clarify the
issues raised by missing data, describing the rationale for MI, the
relationship between the various imputation models and associated
algorithms and its application to increasingly complex data
structures.

Multiple Imputation and its Application:

* Discusses the issues raised by the analysis of partially
observed data, and the assumptions on which analyses rest.

* Presents a practical guide to the issues to consider when
analysing incomplete data from both observational studies and
randomized trials.

* Provides a detailed discussion of the practical use of MI with
real-world examples drawn from medical and social statistics.

* Explores handling non-linear relationships and interactions
with multiple imputation, survival analysis, multilevel multiple
imputation, sensitivity analysis via multiple imputation, using
non-response weights with multiple imputation and doubly robust
multiple imputation.

Multiple Imputation and its Application is aimed at
quantitative researchers and students in the medical and social
sciences with the aim of clarifying the issues raised by the
analysis of incomplete data data, outlining the rationale for MI
and describing how to consider and address the issues that arise in
its application.