Multiple Imputation and its Application (Statistics in Practice)

Data Science: Multiple imputation in practice
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Multiple Imputation and its Application , by James R. Carpenter and Michael G. Kenward, provides an excellent review of multiple imputation MI from basic to advanced concepts. MI is a statistical method for analyzing incomplete data. The flexibility of the MI procedure has prompted its use in a wide variety of applications. This book describes the rationale for MI and its underlying assumptions in a broad range of statistical settings, and demonstrates the use of this procedure for handling missing data in complex data structures.

The text provides a good mixture of theory and practice.

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Read Multiple Imputation and its Application (Statistics in Practice) book reviews & author details and more at tiojusloafillweb.ml Free delivery on qualified orders. Editorial Reviews. From the Back Cover. A practical guide to analysing partially observed data. Collecting, analysing and drawing inferences from data is central .

Throughout the book, the concepts are illustrated with real data examples. The book is divided into three parts: foundations, MI for cross-sectional data, and advanced topics. The first part reviews the basic concepts of missing data, such as types of missing data and missing-data assumptions, and of multiple imputation, such as the MI procedure and its justification. The second part describes the use of MI for handling missing values in cross-sectional data, including the imputation of different types of data continuous, binary, ordinal, etc.

The third part discusses the advanced use of MI for dealing with missing data in complex data structures such as survival data and multilevel data. Other important advanced topics are covered, including the handling of survey weights during imputation, sensitivity analysis, and robust MI. Stata: Data Analysis and Statistical Software. Go Stata. Purchase Products Training Support Company. What are VitalSource eBooks?

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Popular Features. New Releases. Description A practical guide to analysing partially observed data.

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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.

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Ton J. Statistics and Data Interpretation for Social Work. After registration we will ask you to briefly describe your missing data experience none required as well as your expectations from this course. This will require us to create dummy variables for our categorical predictor prog. We appreciate your feedback. Therefore, these cases with a missing outcome could reduce uncertainty when imputing the values of missing covariates in the incomplete cases with an observed outcome patterns 2—3. Analysis of Large and Complex Data.

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 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.

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Data Science: Multiple imputation in practice - Summer Schools in Europe

Bayesian Biostatistics Emmanuel Lesaffre. Multiple Imputation and its Application James Carpenter. Risk Assessment Marvin Rausand. Uncertain Judgements Anthony O'Hagan. Geostatistics for Environmental Scientists Richard Webster.

Multiple Imputation: A Righteous Approach to Handling Missing Data

Back cover copy A practical guide to analysing partially observed data. 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.

Missing data and multiple imputation in clinical epidemiological research

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. Is supported by a supplementary website www. Michael G.

Kenward, Medical Statistics Unit, London School of Hygiene and Tropical Medicine, UK Amongst other areas Professor Kenward has worked in pre-clinical and clinical medicine and epidemiology for over twenty years, holding a number of international positions.

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