A practical guide to analysing partially observeddata.
Collecting, analysing and drawing inferences from data iscentral to research in the medical and social sciences. Unfortunately, it is rarely possible to collect all the intendeddata. The literature on inference from the resultingincomplete data is now huge, and continues to grow both asmethods are developed for large and complex data structures, and asincreasing computer power and suitable software enable researchersto apply these methods.
This book focuses on a particular statistical method foranalysing and drawing inferences from incomplete data, calledMultiple Imputation (MI). MI is attractive because it is bothpractical and widely applicable. The authors aim is to clarify theissues raised by missing data, describing the rationale for MI, therelationship between the various imputation models and associatedalgorithms and its application to increasingly complex datastructures.
Multiple Imputation and its Application:
* Discusses the issues raised by the analysis of partiallyobserved data, and the assumptions on which analyses rest.
* Presents a practical guide to the issues to consider whenanalysing incomplete data from both observational studies andrandomized trials.
* Provides a detailed discussion of the practical use of MI withreal-world examples drawn from medical and social statistics.
* Explores handling non-linear relationships and interactionswith multiple imputation, survival analysis, multilevel multipleimputation, sensitivity analysis via multiple imputation, usingnon-response weights with multiple imputation and doubly robustmultiple imputation.
Multiple Imputation and its Application is aimed atquantitative researchers and students in the medical and socialsciences with the aim of clarifying the issues raised by theanalysis of incomplete data data, outlining the rationale for MIand describing how to consider and address the issues that arise inits application.
Collecting, analysing and drawing inferences from data iscentral to research in the medical and social sciences. Unfortunately, it is rarely possible to collect all the intendeddata. The literature on inference from the resultingincomplete data is now huge, and continues to grow both asmethods are developed for large and complex data structures, and asincreasing computer power and suitable software enable researchersto apply these methods.
This book focuses on a particular statistical method foranalysing and drawing inferences from incomplete data, calledMultiple Imputation (MI). MI is attractive because it is bothpractical and widely applicable. The authors aim is to clarify theissues raised by missing data, describing the rationale for MI, therelationship between the various imputation models and associatedalgorithms and its application to increasingly complex datastructures.
Multiple Imputation and its Application:
* Discusses the issues raised by the analysis of partiallyobserved data, and the assumptions on which analyses rest.
* Presents a practical guide to the issues to consider whenanalysing incomplete data from both observational studies andrandomized trials.
* Provides a detailed discussion of the practical use of MI withreal-world examples drawn from medical and social statistics.
* Explores handling non-linear relationships and interactionswith multiple imputation, survival analysis, multilevel multipleimputation, sensitivity analysis via multiple imputation, usingnon-response weights with multiple imputation and doubly robustmultiple imputation.
Multiple Imputation and its Application is aimed atquantitative researchers and students in the medical and socialsciences with the aim of clarifying the issues raised by theanalysis of incomplete data data, outlining the rationale for MIand describing how to consider and address the issues that arise inits application.
