{"product_id":"missing-data-in-clinical-studies-9780470849811","title":"Missing Data in Clinical Studies","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eMissing Data in Clinical Studies provides a comprehensive account of the problems arising when data from clinical and related studies are incomplete, and presents the reader with approaches to effectively address them. The text provides a critique of conventional and simple methods before moving on to discuss more advanced approaches.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e?Overall, this is an excellent text on missing data that is engaging for practitioners while being rigorous enoughfor use in the graduate biostatistics courses.?(\u003ci\u003eBiometrics\u003c\/i\u003e , September 2009)\" \"\u003ci\u003eMissing Data in Clinical Studies\u003c\/i\u003e does an excellent job of presenting essential ideas on modern concepts and techniques relevant to missing data in clinical studies.\" (\u003ci\u003eJournal of the American Statistician\u003c\/i\u003e, December 2008)  \u003cp\u003e\"?this book is reasonably well organized and covers all the relevant theory and much of the practical applications of the field.\" (\u003ci\u003eJournal of the American Chemical Association\u003c\/i\u003e, August 6, 2008)\u003c\/p\u003e \u003cp\u003e\"Missing Data in Clinical Studies does an excellent job of presenting essential ideas on modern concepts and techniques relevant to missing data in clinical studies.\"  (\u003ci\u003eJournal of the American Statistician,\u003c\/i\u003e December 2008)\u003c\/p\u003e \u003cp\u003e\"Clear, generally accessible and well written, and the content is rich.  This text is a highly recommendable addition to the shelves of practicing statisticians.\" (\u003ci\u003eJournal of Applied Statistics,\u003c\/i\u003e August 2008)\u003c\/p\u003e \u003cp\u003e\"The authors give key examples in the form of several clinical trials and their analyses using the appropriate remedial techniques.\"  (\u003ci\u003eJournal of Tropical Pediatrics\u003c\/i\u003e, August 2007)\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003ePreface.  \u003cp\u003eAcknowledgements.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eI Preliminaries.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Introduction.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 From Imbalance to the Field of Missing Data Research.\u003c\/p\u003e \u003cp\u003e1.2 Incomplete Data in Clinical Studies.\u003c\/p\u003e \u003cp\u003e1.3 MAR, MNAR, and Sensitivity Analysis.\u003c\/p\u003e \u003cp\u003e1.4 Outline of the Book.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Key Examples.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction.\u003c\/p\u003e \u003cp\u003e2.2 The Vorozole Study.\u003c\/p\u003e \u003cp\u003e2.3 The Orthodontic Growth Data.\u003c\/p\u003e \u003cp\u003e2.4 Mastitis in Dairy Cattle.\u003c\/p\u003e \u003cp\u003e2.5 The Depression Trials.\u003c\/p\u003e \u003cp\u003e2.6 The Fluvoxamine Trial.\u003c\/p\u003e \u003cp\u003e2.7 The Toenail Data.\u003c\/p\u003e \u003cp\u003e2.8 Age-Related Macular Degeneration Trial.\u003c\/p\u003e \u003cp\u003e2.9 The Analgesic Trial.\u003c\/p\u003e \u003cp\u003e2.10 The Slovenian Public Opinion Survey.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Terminology and Framework.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Modelling Incompleteness.\u003c\/p\u003e \u003cp\u003e3.2 Terminology.\u003c\/p\u003e \u003cp\u003e3.3 Missing Data Frameworks.\u003c\/p\u003e \u003cp\u003e3.4 Missing Data Mechanisms.\u003c\/p\u003e \u003cp\u003e3.5 Ignorability.\u003c\/p\u003e \u003cp\u003e3.6 Pattern-Mixture Models.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eII Classical Techniques and the Need for Modelling.\u003cbr\u003e \u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 A Perspective on Simple Methods.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction.\u003c\/p\u003e \u003cp\u003e4.2 Simple Methods.\u003c\/p\u003e \u003cp\u003e4.3 Problems with Complete Case Analysis and Last Observation Carried Forward.\u003c\/p\u003e \u003cp\u003e4.4 Using the Available Cases: a Frequentist versus a Likelihood Perspective.\u003c\/p\u003e \u003cp\u003e4.5 Intention to Treat.\u003c\/p\u003e \u003cp\u003e4.6 Concluding Remarks.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Analysis of the Orthodontic Growth Data.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction and Models.\u003c\/p\u003e \u003cp\u003e5.2 The Original, Complete Data.\u003c\/p\u003e \u003cp\u003e5.3 Direct Likelihood.\u003c\/p\u003e \u003cp\u003e5.4 Comparison of Analyses.\u003c\/p\u003e \u003cp\u003e5.5 Example SAS Code for Multivariate Linear Models.\u003c\/p\u003e \u003cp\u003e5.6 Comparative Power under Different Covariance Structures.\u003c\/p\u003e \u003cp\u003e5.7 Concluding Remarks.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Analysis of the Depression Trials.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 View 1: Longitudinal Analysis.\u003c\/p\u003e \u003cp\u003e6.2 Views 2a and 2b and All versus Two Treatment Arms.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eIII Missing at Random and Ignorability.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 The Direct Likelihood Method.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction.\u003c\/p\u003e \u003cp\u003e7.2 Ignorable Analyses in Practice.\u003c\/p\u003e \u003cp\u003e7.3 The Linear Mixed Model.\u003c\/p\u003e \u003cp\u003e7.4 Analysis of the Toenail Data.\u003c\/p\u003e \u003cp\u003e7.5 The Generalized Linear Mixed Model.\u003c\/p\u003e \u003cp\u003e7.6 The Depression Trials.\u003c\/p\u003e \u003cp\u003e7.7 The Analgesic Trial.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 The Expectation–Maximization Algorithm.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction.\u003c\/p\u003e \u003cp\u003e8.2 The Algorithm.\u003c\/p\u003e \u003cp\u003e8.3 Missing Information.\u003c\/p\u003e \u003cp\u003e8.4 Rate of Convergence.\u003c\/p\u003e \u003cp\u003e8.5 EM Acceleration.\u003c\/p\u003e \u003cp\u003e8.6 Calculation of Precision Estimates.\u003c\/p\u003e \u003cp\u003e8.7 A Simple Illustration.\u003c\/p\u003e \u003cp\u003e8.8 Concluding Remarks.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Multiple Imputation.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction.\u003c\/p\u003e \u003cp\u003e9.2 The Basic Procedure.\u003c\/p\u003e \u003cp\u003e9.3 Theoretical Justification.\u003c\/p\u003e \u003cp\u003e9.4 Inference under Multiple Imputation.\u003c\/p\u003e \u003cp\u003e9.5 Efficiency.\u003c\/p\u003e \u003cp\u003e9.6 Making Proper Imputations.\u003c\/p\u003e \u003cp\u003e9.7 Some Roles for Multiple Imputation.\u003c\/p\u003e \u003cp\u003e9.8 Concluding Remarks.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Weighted Estimating Equations.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction.\u003c\/p\u003e \u003cp\u003e10.2 Inverse Probability Weighting.\u003c\/p\u003e \u003cp\u003e10.3 Generalized Estimating Equations for Marginal Models.\u003c\/p\u003e \u003cp\u003e10.4 Weighted Generalized Estimating Equations.\u003c\/p\u003e \u003cp\u003e10.5 The Depression Trials.\u003c\/p\u003e \u003cp\u003e10.6 The Analgesic Trial.\u003c\/p\u003e \u003cp\u003e10.7 Double Robustness.\u003c\/p\u003e \u003cp\u003e10.8 Concluding Remarks.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Combining GEE and MI.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction.\u003c\/p\u003e \u003cp\u003e11.2 Data Generation and Fitting.\u003c\/p\u003e \u003cp\u003e11.3 MI-GEE and MI-Transition.\u003c\/p\u003e \u003cp\u003e11.4 An Asymptotic Simulation Study.\u003c\/p\u003e \u003cp\u003e11.5 Concluding Remarks.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Likelihood-Based Frequentist Inference.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e12.1 Introduction.\u003c\/p\u003e \u003cp\u003e12.2 Information and Sampling Distributions.\u003c\/p\u003e \u003cp\u003e12.3 Bivariate Normal Data.\u003c\/p\u003e \u003cp\u003e12.4 Bivariate Binary Data.\u003c\/p\u003e \u003cp\u003e12.5 Implications for Standard Software.\u003c\/p\u003e \u003cp\u003e12.6 Analysis of the Fluvoxamine Trial.\u003c\/p\u003e \u003cp\u003e12.7 The Muscatine Coronary Risk Factor Study.\u003c\/p\u003e \u003cp\u003e12.8 The Crépeau Data.\u003c\/p\u003e \u003cp\u003e12.9 Concluding Remarks.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Analysis of the Age-Related Macular Degeneration\u003c\/b\u003e \u003cb\u003eTrial.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e13.1 Introduction.\u003c\/p\u003e \u003cp\u003e13.2 Direct Likelihood Analysis of the Continuous Outcome.\u003c\/p\u003e \u003cp\u003e13.3 Weighted Generalized Estimating Equations.\u003c\/p\u003e \u003cp\u003e13.4 Direct Likelihood Analysis of the Binary Outcome.\u003c\/p\u003e \u003cp\u003e13.5 Multiple Imputation.\u003c\/p\u003e \u003cp\u003e13.6 Concluding Remarks.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Incomplete Data and SAS.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e14.1 Introduction.\u003c\/p\u003e \u003cp\u003e14.2 Complete Case Analysis.\u003c\/p\u003e \u003cp\u003e14.3 Last Observation Carried Forward.\u003c\/p\u003e \u003cp\u003e14.4 Direct Likelihood.\u003c\/p\u003e \u003cp\u003e14.5 Weighted Estimating Equations.\u003c\/p\u003e \u003cp\u003e14.6 Multiple Imputation.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eIV Missing Not at Random.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 Selection Models.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e15.1 Introduction.\u003c\/p\u003e \u003cp\u003e15.2 The Diggle–Kenward Model for Continuous Outcomes.\u003c\/p\u003e \u003cp\u003e15.3 Illustration and SAS Implementation.\u003c\/p\u003e \u003cp\u003e15.4 An MNAR Dale Model.\u003c\/p\u003e \u003cp\u003e15.5 A Model for Non-monotone Missingness.\u003c\/p\u003e \u003cp\u003e15.6 Concluding Remarks.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e16 Pattern-Mixture Models.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e16.1 Introduction.\u003c\/p\u003e \u003cp\u003e16.2 A Simple Gaussian Illustration.\u003c\/p\u003e \u003cp\u003e16.3 A Paradox.\u003c\/p\u003e \u003cp\u003e16.4 Strategies to Fit Pattern-Mixture Models.\u003c\/p\u003e \u003cp\u003e16.5 Applying Identifying Restrictions.\u003c\/p\u003e \u003cp\u003e16.6 Pattern-Mixture Analysis of the Vorozole Study.\u003c\/p\u003e \u003cp\u003e16.7 A Clinical Trial in Alzheimer’s Disease.\u003c\/p\u003e \u003cp\u003e16.8 Analysis of the Fluvoxamine Trial.\u003c\/p\u003e \u003cp\u003e16.9 Concluding Remarks.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e17 Shared-Parameter Models.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e18 Protective Estimation.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e18.1 Introduction.\u003c\/p\u003e \u003cp\u003e18.2 Brown’s Protective Estimator for Gaussian Data.\u003c\/p\u003e \u003cp\u003e18.3 A Protective Estimator for Categorical Data.\u003c\/p\u003e \u003cp\u003e18.4 A Protective Estimator for Gaussian Data.\u003c\/p\u003e \u003cp\u003e18.5 Concluding Remarks.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eV Sensitivity Analysis.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e19 MNAR, MAR, and the Nature of Sensitivity.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e19.1 Introduction.\u003c\/p\u003e \u003cp\u003e19.2 Every MNAR Model Has an MAR Bodyguard.\u003c\/p\u003e \u003cp\u003e19.3 The General Case of Incomplete Contingency Tables.\u003c\/p\u003e \u003cp\u003e19.4 The Slovenian Public Opinion Survey.\u003c\/p\u003e \u003cp\u003e19.5 Implications for Formal and Informal Model Selection.\u003c\/p\u003e \u003cp\u003e19.6 Behaviour of the Likelihood Ratio Test for MAR versus MNAR.\u003c\/p\u003e \u003cp\u003e19.7 Concluding Remarks.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e20 Sensitivity Happens.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e20.1 Introduction.\u003c\/p\u003e \u003cp\u003e20.2 A Range of MNAR Models.\u003c\/p\u003e \u003cp\u003e20.3 Identifiability Problems.\u003c\/p\u003e \u003cp\u003e20.4 Analysis of the Fluvoxamine Trial.\u003c\/p\u003e \u003cp\u003e20.5 Concluding Remarks.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e21 Regions of Ignorance and Uncertainty.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e21.1 Introduction.\u003c\/p\u003e \u003cp\u003e21.2 Prevalence of HIV in Kenya.\u003c\/p\u003e \u003cp\u003e21.3 Uncertainty and Sensitivity.\u003c\/p\u003e \u003cp\u003e21.4 Models for Monotone Patterns.\u003c\/p\u003e \u003cp\u003e21.5 Models for Non-monotone Patterns.\u003c\/p\u003e \u003cp\u003e21.6 Formalizing Ignorance and Uncertainty.\u003c\/p\u003e \u003cp\u003e21.7 Analysis of the Fluvoxamine Trial.\u003c\/p\u003e \u003cp\u003e21.8 Artificial Examples.\u003c\/p\u003e \u003cp\u003e21.9 The Slovenian Public Opinion Survey.\u003c\/p\u003e \u003cp\u003e21.10 Some Theoretical Considerations.\u003c\/p\u003e \u003cp\u003e21.11 Concluding Remarks.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e22 Local and Global Influence Methods.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e22.1 Introduction.\u003c\/p\u003e \u003cp\u003e22.2 Gaussian Outcomes.\u003c\/p\u003e \u003cp\u003e22.3 Mastitis in Dairy Cattle.\u003c\/p\u003e \u003cp\u003e22.4 Alternative Local Influence Approaches.\u003c\/p\u003e \u003cp\u003e22.5 The Milk Protein Content Trial.\u003c\/p\u003e \u003cp\u003e22.6 Analysis of the Depression Trials.\u003c\/p\u003e \u003cp\u003e22.7 A Local Influence Approach for Ordinal Data with Dropout.\u003c\/p\u003e \u003cp\u003e22.8 Analysis of the Fluvoxamine Data.\u003c\/p\u003e \u003cp\u003e22.9 A Local Influence Approach for Incomplete Binary Data.\u003c\/p\u003e \u003cp\u003e22.10 Analysis of the Fluvoxamine Data.\u003c\/p\u003e \u003cp\u003e22.11 Concluding Remarks.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e23 The Nature of Local Influence.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e23.1 Introduction.\u003c\/p\u003e \u003cp\u003e23.2 The Rats Data.\u003c\/p\u003e \u003cp\u003e23.3 Analysis and Sensitivity Analysis of the Rats Data.\u003c\/p\u003e \u003cp\u003e23.4 Local Influence Methods and Their Behaviour.\u003c\/p\u003e \u003cp\u003e23.5 Concluding Remarks.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e24 A Latent-Class Mixture Model for Incomplete\u003c\/b\u003e \u003cb\u003eLongitudinal Gaussian Data.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e24.1 Introduction.\u003c\/p\u003e \u003cp\u003e24.2 Latent-Class Mixture Models.\u003c\/p\u003e \u003cp\u003e24.3 The Likelihood Function and Estimation.\u003c\/p\u003e \u003cp\u003e24.4 Classification.\u003c\/p\u003e \u003cp\u003e24.5 Simulation Study.\u003c\/p\u003e \u003cp\u003e24.6 Analysis of the Depression Trials.\u003c\/p\u003e \u003cp\u003e24.7 Concluding Remarks.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eVI Case Studies.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e25 The Age-Related Macular Degeneration Trial.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e25.1 Selection Models and Local Influence.\u003c\/p\u003e \u003cp\u003e25.2 Local Influence Analysis.\u003c\/p\u003e \u003cp\u003e25.3 Pattern-Mixture Models.\u003c\/p\u003e \u003cp\u003e25.4 Concluding Remarks.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e26 The Vorozole Study.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e26.1 Introduction.\u003c\/p\u003e \u003cp\u003e26.2 Exploring the Vorozole Data.\u003c\/p\u003e \u003cp\u003e26.3 A Selection Model for the Vorozole Study.\u003c\/p\u003e \u003cp\u003e26.4 A Pattern-Mixture Model for the Vorozole Study.\u003c\/p\u003e \u003cp\u003e26.5 Concluding Remarks.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003eIndex.\u003c\/p\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":49402440909143,"sku":"9780470849811","price":73.76,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780470849811.jpg?v=1730480403","url":"https:\/\/bookcurl.com\/products\/missing-data-in-clinical-studies-9780470849811","provider":"Book Curl","version":"1.0","type":"link"}