Book description
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.
