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040 _aDLC
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019 _a961620131
_a962566545
020 _a9781118762530
_q(ePub)
020 _a1118762533
_q(ePub)
020 _a9781118762509
_q(Adobe PDF)
020 _a1118762509
_q(Adobe PDF)
020 _a9781118762516
_q(electronic bk.)
020 _a1118762517
_q(electronic bk.)
020 _a1118460707
_q(hbk.)
020 _a9781118460702
_q(hbk.)
020 _a9781306473118
_q(MyiLibrary)
020 _a130647311X
_q(MyiLibrary)
020 _z9781118460702
_q(hardback)
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035 _a(OCoLC)861789127
_z(OCoLC)961620131
_z(OCoLC)962566545
042 _apcc
050 0 0 _aR853.C55
060 1 0 _aQV 771.4
072 7 _aHEA
_x012000
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072 7 _aHEA
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072 7 _aMED
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072 7 _aMED
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082 0 0 _a610.72/4
_223
049 _aMAIN
100 1 _aO'Kelly, Michael,
_eauthor.
245 1 0 _aClinical trials with missing data :
_ba guide for practitioners /
_cMichael O'Kelly, Bohdana Ratitch.
264 1 _aChichester, West Sussex :
_bJohn Wiley & Sons Inc.,
_c2014.
300 _a1 online resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
490 1 _aStatistics in practice
504 _aIncludes bibliographical references and index.
520 _a"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.
588 0 _aPrint version record and CIP data provided by publisher.
505 0 _aSeries; 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
505 8 _a1.8 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
505 8 _aChapter 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?
505 8 _a5.4 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
650 0 _aClinical trials.
650 2 _aClinical Trials as Topic.
650 2 _aBias (Epidemiology)
650 2 _aModels, Statistical.
650 2 _aResearch Design.
650 4 _aBias (Epidemiology)
650 7 _aHEALTH & FITNESS
_xHolism.
_2bisacsh
650 7 _aHEALTH & FITNESS
_xReference.
_2bisacsh
650 7 _aMEDICAL
_xAlternative Medicine.
_2bisacsh
650 7 _aMEDICAL
_xAtlases.
_2bisacsh
650 7 _aMEDICAL
_xEssays.
_2bisacsh
650 7 _aMEDICAL
_xFamily & General Practice.
_2bisacsh
650 7 _aMEDICAL
_xHolistic Medicine.
_2bisacsh
650 7 _aMEDICAL
_xOsteopathy.
_2bisacsh
650 7 _aClinical trials.
_2fast
_0(OCoLC)fst00864429
650 7 _aKlinisches Experiment.
_0(DE-588)4164223-5
_2gnd
650 7 _aStatistik.
_0(DE-588)4056995-0
_2gnd
650 7 _aBias.
_0(DE-588)4133271-4
_2gnd
650 7 _aForschungsplanung.
_0(DE-588)4155051-1
_2gnd
655 4 _aElectronic books.
700 1 _aRatitch, Bohdana,
_eauthor.
776 0 8 _iPrint version:
_aO'Kelly, Michael, author.
_tClinical trials with missing data.
_dChichester, West Sussex : John Wiley & Sons Inc., 2014
_z9781118460702
_w(DLC) 2013041088
830 0 _aStatistics in practice.
856 4 0 _uhttp://onlinelibrary.wiley.com/book/10.1002/9781118762516
_zWiley Online Library [Free Download only for SUST IP]
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