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| 001 | ocn861789127 | ||
| 003 | OCoLC | ||
| 005 | 20171105115214.0 | ||
| 006 | m o d | ||
| 007 | cr ||||||||||| | ||
| 008 | 131029s2014 enk ob 001 0 eng | ||
| 010 | _a 2013043089 | ||
| 020 |
_a9781118762530 _q(ePub) |
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_a1118762509 _q(Adobe PDF) |
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_a9781118460702 _q(hbk.) |
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_a(OCoLC)861789127 _z(OCoLC)961620131 _z(OCoLC)962566545 |
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| 040 |
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| 042 | _apcc | ||
| 049 | _aMAIN | ||
| 050 | 0 | 0 | _aR853.C55 |
| 060 | 1 | 0 | _aQV 771.4 |
| 072 | 7 |
_aHEA _x012000 _2bisacsh |
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_aHEA _x020000 _2bisacsh |
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_aMED _x004000 _2bisacsh |
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| 082 | 0 | 0 |
_a610.72/4 _223 |
| 100 | 1 |
_aO'Kelly, Michael, _eauthor. |
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| 245 | 1 | 0 |
_aClinical trials with missing data : a guide for practitioners / _cMichael O'Kelly, Bohdana Ratitch. _h[electronic resource] |
| 264 | 1 |
_aChichester, West Sussex : _bJohn Wiley & Sons Inc., _c2014. |
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| 300 | _a1 online resource. | ||
| 336 |
_atext _btxt _2rdacontent |
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| 337 |
_acomputer _bc _2rdamedia |
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| 338 |
_aonline resource _bcr _2rdacarrier |
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| 490 | 1 | _aStatistics in practice | |
| 504 | _aIncludes bibliographical references and index. | ||
| 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 | |
| 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. | |
| 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 |
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| 650 | 7 |
_aHEALTH & FITNESS _xReference. _2bisacsh |
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| 650 | 7 |
_aMEDICAL _xAlternative Medicine. _2bisacsh |
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| 650 | 7 |
_aMEDICAL _xAtlases. _2bisacsh |
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| 650 | 7 |
_aMEDICAL _xEssays. _2bisacsh |
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| 650 | 7 |
_aMEDICAL _xFamily & General Practice. _2bisacsh |
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| 650 | 7 |
_aMEDICAL _xHolistic Medicine. _2bisacsh |
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| 650 | 7 |
_aMEDICAL _xOsteopathy. _2bisacsh |
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| 650 | 7 |
_aClinical trials. _2fast _0(OCoLC)fst00864429 |
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| 650 | 7 |
_aKlinisches Experiment. _0(DE-588)4164223-5 _2gnd |
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| 650 | 7 |
_aStatistik. _0(DE-588)4056995-0 _2gnd |
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| 650 | 7 |
_aBias. _0(DE-588)4133271-4 _2gnd |
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| 650 | 7 |
_aForschungsplanung. _0(DE-588)4155051-1 _2gnd |
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| 655 | 4 | _aElectronic books. | |
| 700 | 1 |
_aRatitch, Bohdana, _eauthor. |
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| 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 |
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_c207054 _d207054 |
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