rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Clinical trials with missing data
    sub title: a guide for practitioners
  name:
    name part: O'Kelly, Michael
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: Ratitch, Bohdana
    role:
      role term: author.
  type of resource: text
  genre: bibliography
  genre: Electronic books.
  origin info:
    place:
      place term: enk
    date issued: 2014
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource.
  abstract: "This book provides practical guidance for statisticians, clinicians, and researchers involved in clinical trials in the biopharmaceutical industry, medical and public health organisations. Academics and students needing an introduction to handling missing data will also find this book invaluable. The authors describe how missing data can affect the outcome and credibility of a clinical trial, show by examples how a clinical team can work to prevent missing data, and present the reader with approaches to address missing data effectively. The book is illustrated throughout with realistic case studies and worked examples, and presents clear and concise guidelines to enable good planning for missing data. The authors show how to handle missing data in a way that is transparent and easy to understand for clinicians, regulators and patients. New developments are presented to improve the choice and implementation of primary and sensitivity analyses for missing data. Many SAS code examples are included - the reader is given a toolbox for implementing analyses under a variety of assumptions"--Provided by publisher.
  table of contents: Series; Title Page; Copyright; Dedication; Preface; References; Acknowledgments; Notation; Table of SAS code fragments; Contributors; Chapter 1: What's the problem with missing data?; 1.1 What do we mean by missing data?; 1.2 An illustration; 1.3 Why can't I use only the available primary endpoint data?; 1.4 What's the problem with using last observation carried forward?; 1.5 Can we just assume that data are missing at random?; 1.6 What can be done if data may be missing not at random?; 1.7 Stress-testing study results for robustness to missing data
  table of contents: 1.8 How the pattern of dropouts can bias the outcome1.9 How do we formulate a strategy for missing data?; 1.10 Description of example datasets; Appendix 1.A: Formal definitions of MCAR, MAR and MNAR; References; Chapter 2: The prevention of missing data; 2.1 Introduction; 2.2 The impact of "too much" missing data; 2.3 The role of the statistician in the prevention of missing data; 2.4 Methods for increasing subject retention; 2.5 Improving understanding of reasons for subject withdrawal; Acknowledgments; Appendix 2.A: Example protocol text for missing data prevention; References
  table of contents: Chapter 3: Regulatory guidance -- a quick tour3.1 International conference on harmonization guideline: Statistical principles for clinical trials: E9; 3.2 The US and EU regulatory documents; 3.3 Key points in the regulatory documents on missing data; 3.4 Regulatory guidance on particular statistical approaches; 3.5 Guidance about how to plan for missing data in a study; 3.6 Differences in emphasis between the NRC report and EU guidance documents; 3.7 Other technical points from the NRC report; 3.8 Other US/EU/international guidance documents that refer to missing data; 3.9 And in practice?
  table of contents: 5.4 Applying the mixed model for repeated measures5.5 Additional mixed model for repeated measures topics; 5.6 Logistic regression mixed model for repeated measures using the generalized linear mixed model; References; Table of SAS Code Fragments; Chapter 6: Multiple imputation; 6.1 Introduction; 6.2 Imputation phase; 6.3 Analysis phase: Analyzing multiple imputed datasets; 6.4 Pooling phase: Combining results from multiple datasets; 6.5 Required number of imputations; 6.6 Some practical considerations; 6.7 Pre-specifying details of analysis with multiple imputation
  note: Michael O'Kelly, Bohdana Ratitch.
  note: Includes bibliographical references and index.
  subject:
    topic: Clinical trials
  subject:
    topic: Clinical Trials as Topic
  subject:
    topic: Bias (Epidemiology)
  subject:
    topic: Models, Statistical
  subject:
    topic: Research Design
  subject:
    topic: Bias (Epidemiology)
  subject:
    topic: HEALTH & FITNESS
    topic: Holism
  subject:
    topic: HEALTH & FITNESS
    topic: Reference
  subject:
    topic: MEDICAL
    topic: Alternative Medicine
  subject:
    topic: MEDICAL
    topic: Atlases
  subject:
    topic: MEDICAL
    topic: Essays
  subject:
    topic: MEDICAL
    topic: Family & General Practice
  subject:
    topic: MEDICAL
    topic: Holistic Medicine
  subject:
    topic: MEDICAL
    topic: Osteopathy
  subject:
    topic: Clinical trials
  subject:
    topic: Klinisches Experiment
  subject:
    topic: Statistik
  subject:
    topic: Bias
  subject:
    topic: Forschungsplanung
  classification: R853.C55
  classification: 610.72/4
  classification: QV 771.4
  related item:
    title info:
      title: Clinical trials with missing data
    name:
      name part: O'Kelly, Michael, author.
    origin info:
      publisher: Chichester, West Sussex : John Wiley & Sons Inc., 2014
    identifier: (DLC) 2013041088
  related item:
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  record info:
    record content source: DLC
    record creation date: 131029
    record change date: 20170124071849.4
    record identifier: ocn861789127
    language of cataloging:
      language term: eng