Logistic Regression With Missing Values In The Covariates

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Logistic Regression with Missing Values in the Covariates

Author : Werner Vach
Publisher : Springer Science & Business Media
ISBN 13 : 1461226503
Total Pages : 152 pages
Book Rating : 4.4/5 (612 download)

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Book Synopsis Logistic Regression with Missing Values in the Covariates by : Werner Vach

Download or read book Logistic Regression with Missing Values in the Covariates written by Werner Vach and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 152 pages. Available in PDF, EPUB and Kindle. Book excerpt: In many areas of science a basic task is to assess the influence of several factors on a quantity of interest. If this quantity is binary logistic, regression models provide a powerful tool for this purpose. This monograph presents an account of the use of logistic regression in the case where missing values in the variables prevent the use of standard techniques. Such situations occur frequently across a wide range of statistical applications. The emphasis of this book is on methods related to the classical maximum likelihood principle. The author reviews the essentials of logistic regression and discusses the variety of mechanisms which might cause missing values while the rest of the book covers the methods which may be used to deal with missing values and their effectiveness. Researchers across a range of disciplines and graduate students in statistics and biostatistics will find this a readable account of this.

Multiple Imputation of Missing Data Using SAS

Author : Patricia Berglund
Publisher : SAS Institute
ISBN 13 : 162959203X
Total Pages : 164 pages
Book Rating : 4.6/5 (295 download)

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Book Synopsis Multiple Imputation of Missing Data Using SAS by : Patricia Berglund

Download or read book Multiple Imputation of Missing Data Using SAS written by Patricia Berglund and published by SAS Institute. This book was released on 2014-07-01 with total page 164 pages. Available in PDF, EPUB and Kindle. Book excerpt: Find guidance on using SAS for multiple imputation and solving common missing data issues. Multiple Imputation of Missing Data Using SAS provides both theoretical background and constructive solutions for those working with incomplete data sets in an engaging example-driven format. It offers practical instruction on the use of SAS for multiple imputation and provides numerous examples that use a variety of public release data sets with applications to survey data. Written for users with an intermediate background in SAS programming and statistics, this book is an excellent resource for anyone seeking guidance on multiple imputation. The authors cover the MI and MIANALYZE procedures in detail, along with other procedures used for analysis of complete data sets. They guide analysts through the multiple imputation process, including evaluation of missing data patterns, choice of an imputation method, execution of the process, and interpretation of results. Topics discussed include how to deal with missing data problems in a statistically appropriate manner, how to intelligently select an imputation method, how to incorporate the uncertainty introduced by the imputation process, and how to incorporate the complex sample design (if appropriate) through use of the SAS SURVEY procedures. Discover the theoretical background and see extensive applications of the multiple imputation process in action. This book is part of the SAS Press program.

Model-Free Curve Estimation

Author : Michael E. Tarter
Publisher : CRC Press
ISBN 13 : 9780412042515
Total Pages : 304 pages
Book Rating : 4.0/5 (425 download)

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Book Synopsis Model-Free Curve Estimation by : Michael E. Tarter

Download or read book Model-Free Curve Estimation written by Michael E. Tarter and published by CRC Press. This book was released on 1993-12-16 with total page 304 pages. Available in PDF, EPUB and Kindle. Book excerpt: Model-Free Curve Estimation details the Fourier series approach to density estimation and explores how model-free technology can be expanded to deal with other statistical curves, such as survival and regression functions. It also describes the implementation of some curves for exploratory data analysis, including a specialized curve for detecting and analyzing hidden subpopulations in data and a family of curves useful for finding the best transformation and model to use in a statistical analysis.

Flexible Imputation of Missing Data, Second Edition

Author : Stef van Buuren
Publisher : CRC Press
ISBN 13 : 0429960352
Total Pages : 444 pages
Book Rating : 4.4/5 (299 download)

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Book Synopsis Flexible Imputation of Missing Data, Second Edition by : Stef van Buuren

Download or read book Flexible Imputation of Missing Data, Second Edition written by Stef van Buuren and published by CRC Press. This book was released on 2018-07-17 with total page 444 pages. Available in PDF, EPUB and Kindle. Book excerpt: Missing data pose challenges to real-life data analysis. Simple ad-hoc fixes, like deletion or mean imputation, only work under highly restrictive conditions, which are often not met in practice. Multiple imputation replaces each missing value by multiple plausible values. The variability between these replacements reflects our ignorance of the true (but missing) value. Each of the completed data set is then analyzed by standard methods, and the results are pooled to obtain unbiased estimates with correct confidence intervals. Multiple imputation is a general approach that also inspires novel solutions to old problems by reformulating the task at hand as a missing-data problem. This is the second edition of a popular book on multiple imputation, focused on explaining the application of methods through detailed worked examples using the MICE package as developed by the author. This new edition incorporates the recent developments in this fast-moving field. This class-tested book avoids mathematical and technical details as much as possible: formulas are accompanied by verbal statements that explain the formula in accessible terms. The book sharpens the reader’s intuition on how to think about missing data, and provides all the tools needed to execute a well-grounded quantitative analysis in the presence of missing data.

Logistic Regression with Incompletely Observed Binary Covariates

Author : Hai-An Hsu
Publisher :
ISBN 13 :
Total Pages : 254 pages
Book Rating : 4.:/5 (341 download)

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Book Synopsis Logistic Regression with Incompletely Observed Binary Covariates by : Hai-An Hsu

Download or read book Logistic Regression with Incompletely Observed Binary Covariates written by Hai-An Hsu and published by . This book was released on 1995 with total page 254 pages. Available in PDF, EPUB and Kindle. Book excerpt: Logistic regression is one of the most important tools in the analysis of epidemiological and clinical data. Such data often contain missing values for one or more variables. Common practice is to eliminate all individuals for whom any information is missing. This deletion approach does not make efficient use of available information and often introduces bias.

Logistic Regression with Missing Covariate Data

Author : Marjorie Ireland
Publisher :
ISBN 13 :
Total Pages : 242 pages
Book Rating : 4.:/5 (328 download)

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Book Synopsis Logistic Regression with Missing Covariate Data by : Marjorie Ireland

Download or read book Logistic Regression with Missing Covariate Data written by Marjorie Ireland and published by . This book was released on 1995 with total page 242 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Multiple Imputation and its Application

Author : James Carpenter
Publisher : John Wiley & Sons
ISBN 13 : 1119942276
Total Pages : 368 pages
Book Rating : 4.1/5 (199 download)

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Book Synopsis Multiple Imputation and its Application by : James Carpenter

Download or read book Multiple Imputation and its Application written by James Carpenter and published by John Wiley & Sons. This book was released on 2012-12-21 with total page 368 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Multiple Imputation of Missing Data in Practice

Author : Yulei He
Publisher : CRC Press
ISBN 13 : 0429530978
Total Pages : 419 pages
Book Rating : 4.4/5 (295 download)

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Book Synopsis Multiple Imputation of Missing Data in Practice by : Yulei He

Download or read book Multiple Imputation of Missing Data in Practice written by Yulei He and published by CRC Press. This book was released on 2021-11-20 with total page 419 pages. Available in PDF, EPUB and Kindle. Book excerpt: Multiple Imputation of Missing Data in Practice: Basic Theory and Analysis Strategies provides a comprehensive introduction to the multiple imputation approach to missing data problems that are often encountered in data analysis. Over the past 40 years or so, multiple imputation has gone through rapid development in both theories and applications. It is nowadays the most versatile, popular, and effective missing-data strategy that is used by researchers and practitioners across different fields. There is a strong need to better understand and learn about multiple imputation in the research and practical community. Accessible to a broad audience, this book explains statistical concepts of missing data problems and the associated terminology. It focuses on how to address missing data problems using multiple imputation. It describes the basic theory behind multiple imputation and many commonly-used models and methods. These ideas are illustrated by examples from a wide variety of missing data problems. Real data from studies with different designs and features (e.g., cross-sectional data, longitudinal data, complex surveys, survival data, studies subject to measurement error, etc.) are used to demonstrate the methods. In order for readers not only to know how to use the methods, but understand why multiple imputation works and how to choose appropriate methods, simulation studies are used to assess the performance of the multiple imputation methods. Example datasets and sample programming code are either included in the book or available at a github site (https://github.com/he-zhang-hsu/multiple_imputation_book). Key Features Provides an overview of statistical concepts that are useful for better understanding missing data problems and multiple imputation analysis Provides a detailed discussion on multiple imputation models and methods targeted to different types of missing data problems (e.g., univariate and multivariate missing data problems, missing data in survival analysis, longitudinal data, complex surveys, etc.) Explores measurement error problems with multiple imputation Discusses analysis strategies for multiple imputation diagnostics Discusses data production issues when the goal of multiple imputation is to release datasets for public use, as done by organizations that process and manage large-scale surveys with nonresponse problems For some examples, illustrative datasets and sample programming code from popular statistical packages (e.g., SAS, R, WinBUGS) are included in the book. For others, they are available at a github site (https://github.com/he-zhang-hsu/multiple_imputation_book)

Logistic Regression Analysis with Missing Values

Author :
Publisher :
ISBN 13 :
Total Pages : 47 pages
Book Rating : 4.:/5 (113 download)

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Book Synopsis Logistic Regression Analysis with Missing Values by :

Download or read book Logistic Regression Analysis with Missing Values written by and published by . This book was released on 2015 with total page 47 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Analysis of Incomplete Multivariate Data

Author : J.L. Schafer
Publisher : CRC Press
ISBN 13 : 9781439821862
Total Pages : 478 pages
Book Rating : 4.8/5 (218 download)

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Book Synopsis Analysis of Incomplete Multivariate Data by : J.L. Schafer

Download or read book Analysis of Incomplete Multivariate Data written by J.L. Schafer and published by CRC Press. This book was released on 1997-08-01 with total page 478 pages. Available in PDF, EPUB and Kindle. Book excerpt: The last two decades have seen enormous developments in statistical methods for incomplete data. The EM algorithm and its extensions, multiple imputation, and Markov Chain Monte Carlo provide a set of flexible and reliable tools from inference in large classes of missing-data problems. Yet, in practical terms, those developments have had surprisingly little impact on the way most data analysts handle missing values on a routine basis. Analysis of Incomplete Multivariate Data helps bridge the gap between theory and practice, making these missing-data tools accessible to a broad audience. It presents a unified, Bayesian approach to the analysis of incomplete multivariate data, covering datasets in which the variables are continuous, categorical, or both. The focus is applied, where necessary, to help readers thoroughly understand the statistical properties of those methods, and the behavior of the accompanying algorithms. All techniques are illustrated with real data examples, with extended discussion and practical advice. All of the algorithms described in this book have been implemented by the author for general use in the statistical languages S and S Plus. The software is available free of charge on the Internet.

Mixed Effects Models for the Population Approach

Author : Marc Lavielle
Publisher : CRC Press
ISBN 13 : 1482226510
Total Pages : 380 pages
Book Rating : 4.4/5 (822 download)

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Book Synopsis Mixed Effects Models for the Population Approach by : Marc Lavielle

Download or read book Mixed Effects Models for the Population Approach written by Marc Lavielle and published by CRC Press. This book was released on 2014-07-14 with total page 380 pages. Available in PDF, EPUB and Kindle. Book excerpt: Wide-Ranging Coverage of Parametric Modeling in Linear and Nonlinear Mixed Effects ModelsMixed Effects Models for the Population Approach: Models, Tasks, Methods and Tools presents a rigorous framework for describing, implementing, and using mixed effects models. With these models, readers can perform parameter estimation and modeling across a whol

Flexible Imputation of Missing Data

Author : Stef van Buuren
Publisher : CRC Press
ISBN 13 : 1439868255
Total Pages : 326 pages
Book Rating : 4.4/5 (398 download)

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Book Synopsis Flexible Imputation of Missing Data by : Stef van Buuren

Download or read book Flexible Imputation of Missing Data written by Stef van Buuren and published by CRC Press. This book was released on 2012-03-29 with total page 326 pages. Available in PDF, EPUB and Kindle. Book excerpt: Missing data form a problem in every scientific discipline, yet the techniques required to handle them are complicated and often lacking. One of the great ideas in statistical science—multiple imputation—fills gaps in the data with plausible values, the uncertainty of which is coded in the data itself. It also solves other problems, many of which are missing data problems in disguise. Flexible Imputation of Missing Data is supported by many examples using real data taken from the author's vast experience of collaborative research, and presents a practical guide for handling missing data under the framework of multiple imputation. Furthermore, detailed guidance of implementation in R using the author’s package MICE is included throughout the book. Assuming familiarity with basic statistical concepts and multivariate methods, Flexible Imputation of Missing Data is intended for two audiences: (Bio)statisticians, epidemiologists, and methodologists in the social and health sciences Substantive researchers who do not call themselves statisticians, but who possess the necessary skills to understand the principles and to follow the recipes This graduate-tested book avoids mathematical and technical details as much as possible: formulas are accompanied by a verbal statement that explains the formula in layperson terms. Readers less concerned with the theoretical underpinnings will be able to pick up the general idea, and technical material is available for those who desire deeper understanding. The analyses can be replicated in R using a dedicated package developed by the author.

The Prevention and Treatment of Missing Data in Clinical Trials

Author : National Research Council
Publisher : National Academies Press
ISBN 13 : 030918651X
Total Pages : 163 pages
Book Rating : 4.3/5 (91 download)

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Book Synopsis The Prevention and Treatment of Missing Data in Clinical Trials by : National Research Council

Download or read book The Prevention and Treatment of Missing Data in Clinical Trials written by National Research Council and published by National Academies Press. This book was released on 2010-12-21 with total page 163 pages. Available in PDF, EPUB and Kindle. Book excerpt: Randomized clinical trials are the primary tool for evaluating new medical interventions. Randomization provides for a fair comparison between treatment and control groups, balancing out, on average, distributions of known and unknown factors among the participants. Unfortunately, these studies often lack a substantial percentage of data. This missing data reduces the benefit provided by the randomization and introduces potential biases in the comparison of the treatment groups. Missing data can arise for a variety of reasons, including the inability or unwillingness of participants to meet appointments for evaluation. And in some studies, some or all of data collection ceases when participants discontinue study treatment. Existing guidelines for the design and conduct of clinical trials, and the analysis of the resulting data, provide only limited advice on how to handle missing data. Thus, approaches to the analysis of data with an appreciable amount of missing values tend to be ad hoc and variable. The Prevention and Treatment of Missing Data in Clinical Trials concludes that a more principled approach to design and analysis in the presence of missing data is both needed and possible. Such an approach needs to focus on two critical elements: (1) careful design and conduct to limit the amount and impact of missing data and (2) analysis that makes full use of information on all randomized participants and is based on careful attention to the assumptions about the nature of the missing data underlying estimates of treatment effects. In addition to the highest priority recommendations, the book offers more detailed recommendations on the conduct of clinical trials and techniques for analysis of trial data.

The EM Algorithm and Extensions

Author : Geoffrey J. McLachlan
Publisher : John Wiley & Sons
ISBN 13 : 0470191600
Total Pages : 399 pages
Book Rating : 4.4/5 (71 download)

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Book Synopsis The EM Algorithm and Extensions by : Geoffrey J. McLachlan

Download or read book The EM Algorithm and Extensions written by Geoffrey J. McLachlan and published by John Wiley & Sons. This book was released on 2007-11-09 with total page 399 pages. Available in PDF, EPUB and Kindle. Book excerpt: The only single-source——now completely updated and revised——to offer a unified treatment of the theory, methodology, and applications of the EM algorithm Complete with updates that capture developments from the past decade, The EM Algorithm and Extensions, Second Edition successfully provides a basic understanding of the EM algorithm by describing its inception, implementation, and applicability in numerous statistical contexts. In conjunction with the fundamentals of the topic, the authors discuss convergence issues and computation of standard errors, and, in addition, unveil many parallels and connections between the EM algorithm and Markov chain Monte Carlo algorithms. Thorough discussions on the complexities and drawbacks that arise from the basic EM algorithm, such as slow convergence and lack of an in-built procedure to compute the covariance matrix of parameter estimates, are also presented. While the general philosophy of the First Edition has been maintained, this timely new edition has been updated, revised, and expanded to include: New chapters on Monte Carlo versions of the EM algorithm and generalizations of the EM algorithm New results on convergence, including convergence of the EM algorithm in constrained parameter spaces Expanded discussion of standard error computation methods, such as methods for categorical data and methods based on numerical differentiation Coverage of the interval EM, which locates all stationary points in a designated region of the parameter space Exploration of the EM algorithm's relationship with the Gibbs sampler and other Markov chain Monte Carlo methods Plentiful pedagogical elements—chapter introductions, lists of examples, author and subject indices, computer-drawn graphics, and a related Web site The EM Algorithm and Extensions, Second Edition serves as an excellent text for graduate-level statistics students and is also a comprehensive resource for theoreticians, practitioners, and researchers in the social and physical sciences who would like to extend their knowledge of the EM algorithm.

Comparison of Approaches for Handling Missingness in Covariates for Propensity Score Models

Author : Jiangxiu Zhou
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (927 download)

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Book Synopsis Comparison of Approaches for Handling Missingness in Covariates for Propensity Score Models by : Jiangxiu Zhou

Download or read book Comparison of Approaches for Handling Missingness in Covariates for Propensity Score Models written by Jiangxiu Zhou and published by . This book was released on 2015 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Causal effect estimation with observational data is subject to bias due to confounding. Although potential confounders could be adjusted for by fitting a multiple regression model, a more effective way to control for confounding is to use propensity score methods. Propensity scores are most commonly estimated from logistic regression with a binary exposure; generalized propensity scores could be estimated instead using linear regression if the exposure is continuous. One unresolved issue in propensity score estimation is handling of missing values in covariates. As covariates are only used for propensity score estimation but not for later outcome analysis, missing values in covariates may need to be handled differently from missing values in outcome analysis. Several approaches have been proposed for handling covariate missingness, including multiple imputation (MI), multiple imputation with missingness pattern (MIMP) and treatment mean imputation. There are other potentially useful approaches that have not been evaluated, including single imputation, single conditional mean imputation and Generalized Boosted Modeling (GBM), which is a nonparametric approach of estimating propensity scores and missing values are automatically accounted for in the estimation.To evaluate the performance of single imputation, single conditional mean imputation and GBM in comparison to the previously proposed approaches including treatment mean imputation, MI and MIMP, a simulation study is conducted with a binary exposure. Results suggest that when all confounders are included for propensity score estimation, single imputation, single conditional mean imputation, MI and MIMP perform almost equally well and better than treatment mean imputation and GBM. To examine whether the finding could be extended to a continuous exposure setting, another simulation study is conducted. Results suggest that single imputation, single conditional imputation, MI, MIMP and GBM with single conditional mean imputation have equally good and better performance than treatment mean imputation and GBM with incomplete data under scenario A (linearity and additivity). None of the approaches perform well under scenario G (nonlinearity and nonadditivity). These approaches are further demonstrated and compared through an empirical analysis with the Adolescent Alcohol Prevention Trial (AAPT). A similar pattern of results is observed as in the simulation study. It is recommended to impute missing covariates using different approaches and similar estimates help provide more confidence in the estimates.

Statistical Analysis with Missing Data

Author : Roderick J. A. Little
Publisher : John Wiley & Sons
ISBN 13 : 1118595696
Total Pages : 463 pages
Book Rating : 4.1/5 (185 download)

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Book Synopsis Statistical Analysis with Missing Data by : Roderick J. A. Little

Download or read book Statistical Analysis with Missing Data written by Roderick J. A. Little and published by John Wiley & Sons. This book was released on 2019-03-21 with total page 463 pages. Available in PDF, EPUB and Kindle. Book excerpt: An up-to-date, comprehensive treatment of a classic text on missing data in statistics The topic of missing data has gained considerable attention in recent decades. This new edition by two acknowledged experts on the subject offers an up-to-date account of practical methodology for handling missing data problems. Blending theory and application, authors Roderick Little and Donald Rubin review historical approaches to the subject and describe simple methods for multivariate analysis with missing values. They then provide a coherent theory for analysis of problems based on likelihoods derived from statistical models for the data and the missing data mechanism, and then they apply the theory to a wide range of important missing data problems. Statistical Analysis with Missing Data, Third Edition starts by introducing readers to the subject and approaches toward solving it. It looks at the patterns and mechanisms that create the missing data, as well as a taxonomy of missing data. It then goes on to examine missing data in experiments, before discussing complete-case and available-case analysis, including weighting methods. The new edition expands its coverage to include recent work on topics such as nonresponse in sample surveys, causal inference, diagnostic methods, and sensitivity analysis, among a host of other topics. An updated “classic” written by renowned authorities on the subject Features over 150 exercises (including many new ones) Covers recent work on important methods like multiple imputation, robust alternatives to weighting, and Bayesian methods Revises previous topics based on past student feedback and class experience Contains an updated and expanded bibliography The authors were awarded The Karl Pearson Prize in 2017 by the International Statistical Institute, for a research contribution that has had profound influence on statistical theory, methodology or applications. Their work "has been no less than defining and transforming." (ISI) Statistical Analysis with Missing Data, Third Edition is an ideal textbook for upper undergraduate and/or beginning graduate level students of the subject. It is also an excellent source of information for applied statisticians and practitioners in government and industry.

Multiple Imputation for Nonresponse in Surveys

Author : Donald B. Rubin
Publisher : John Wiley & Sons
ISBN 13 : 0470317361
Total Pages : 258 pages
Book Rating : 4.4/5 (73 download)

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Book Synopsis Multiple Imputation for Nonresponse in Surveys by : Donald B. Rubin

Download or read book Multiple Imputation for Nonresponse in Surveys written by Donald B. Rubin and published by John Wiley & Sons. This book was released on 2009-09-25 with total page 258 pages. Available in PDF, EPUB and Kindle. Book excerpt: Demonstrates how nonresponse in sample surveys and censuses can be handled by replacing each missing value with two or more multiple imputations. Clearly illustrates the advantages of modern computing to such handle surveys, and demonstrates the benefit of this statistical technique for researchers who must analyze them. Also presents the background for Bayesian and frequentist theory. After establishing that only standard complete-data methods are needed to analyze a multiply-imputed set, the text evaluates procedures in general circumstances, outlining specific procedures for creating imputations in both the ignorable and nonignorable cases. Examples and exercises reinforce ideas, and the interplay of Bayesian and frequentist ideas presents a unified picture of modern statistics.