Preventing And Treating Missing Data In Longitudinal Clinical Trials
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Preventing And Treating Missing Data In Longitudinal Clinical Trials
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Author : Craig Mallinckrodt
language : en
Publisher:
Release Date : 2013
Preventing And Treating Missing Data In Longitudinal Clinical Trials written by Craig Mallinckrodt and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013 with categories.
Preventing And Treating Missing Data In Longitudinal Clinical Trials
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Author : Craig Mallinckrodt
language : en
Publisher:
Release Date : 2013
Preventing And Treating Missing Data In Longitudinal Clinical Trials written by Craig Mallinckrodt and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013 with Clinical trials categories.
Recent decades have brought advances in statistical theory for missing data, which, combined with advances in computing ability, have allowed implementation of a wide array of analyses. In fact, so many methods are available that it can be difficult to ascertain when to use which method. This book focuses on the prevention and treatment of missing data in longitudinal clinical trials. Based on his extensive experience with missing data, the author offers advice on choosing analysis methods and on ways to prevent missing data through appropriate trial design and conduct. He offers a practical guide to key principles and explains analytic methods for the non-statistician using limited statistical notation and jargon. The book's goal is to present a comprehensive strategy for preventing and treating missing data, and to make available the programs used to conduct the analyses of the example dataset"
Preventing And Treating Missing Data In Longitudinal Clinical Trials
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Author : Craig Mallinckrodt
language : en
Publisher:
Release Date : 2013
Preventing And Treating Missing Data In Longitudinal Clinical Trials written by Craig Mallinckrodt and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013 with Clinical trials categories.
Focuses on the prevention and treatment of missing data in longitudinal clinical trials, looking at key principles and explaining analytic methods.
The Prevention And Treatment Of Missing Data In Clinical Trials
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Author : National Research Council
language : en
Publisher: National Academies Press
Release Date : 2011-01-21
The Prevention And Treatment Of Missing Data In Clinical Trials written by National Research Council and has been published by National Academies Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-01-21 with Medical categories.
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.
Preventing And Treating Missing Data In Longitudinal Clinical Trials
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Author : Craig H. Mallinckrodt
language : en
Publisher: Cambridge University Press
Release Date : 2013-01-28
Preventing And Treating Missing Data In Longitudinal Clinical Trials written by Craig H. Mallinckrodt and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-01-28 with Medical categories.
Recent decades have brought advances in statistical theory for missing data, which, combined with advances in computing ability, have allowed implementation of a wide array of analyses. In fact, so many methods are available that it can be difficult to ascertain when to use which method. This book focuses on the prevention and treatment of missing data in longitudinal clinical trials. Based on his extensive experience with missing data, the author offers advice on choosing analysis methods and on ways to prevent missing data through appropriate trial design and conduct. He offers a practical guide to key principles and explains analytic methods for the non-statistician using limited statistical notation and jargon. The book's goal is to present a comprehensive strategy for preventing and treating missing data, and to make available the programs used to conduct the analyses of the example dataset.
Clinical Trials With Missing Data
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Author : Michael O'Kelly
language : en
Publisher: John Wiley & Sons
Release Date : 2014-04-07
Clinical Trials With Missing Data written by Michael O'Kelly and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-04-07 with Medical categories.
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.
Analyzing Longitudinal Clinical Trial Data
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Author : Craig Mallinckrodt
language : en
Publisher: CRC Press
Release Date : 2016-12-12
Analyzing Longitudinal Clinical Trial Data written by Craig Mallinckrodt and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-12-12 with Mathematics categories.
Analyzing Longitudinal Clinical Trial Data: A Practical Guide provide practical and easy to implement approaches for bringing the latest theory on analysis of longitudinal clinical trial data into routine practice.?This book, with its example-oriented approach that includes numerous SAS and R code fragments, is an essential resource for statisticians and graduate students specializing in medical research. The authors provide clear descriptions of the relevant statistical theory and illustrate practical considerations for modeling longitudinal data. Topics covered include choice of endpoint and statistical test; modeling means and the correlations between repeated measurements; accounting for covariates; modeling categorical data; model verification; methods for incomplete (missing) data that includes the latest developments in sensitivity analyses, along with approaches for and issues in choosing estimands; and means for preventing missing data. Each chapter stands alone in its coverage of a topic. The concluding chapters provide detailed advice on how to integrate these independent topics into an over-arching study development process and statistical analysis plan.
A Mixed Model Approach For Intent To Treat Analysis In Longitudinal Clinical Trials With Missing Values
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Author : Hrishikesh Chakraborty
language : en
Publisher: RTI Press
Release Date : 2009-02-28
A Mixed Model Approach For Intent To Treat Analysis In Longitudinal Clinical Trials With Missing Values written by Hrishikesh Chakraborty and has been published by RTI Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-02-28 with Computers categories.
Missing values and dropouts are common issues in longitudinal studies in all areas of medicine and public health. Intent-to-treat (ITT) analysis has become a widely accepted method for the analysis of controlled clinical trials. In most controlled clinical trials, some patients do not complete their intended followup according to the protocol for a variety of reasons; this problem generates missing values. Missing values lead to concern and confusion in identifying the ITT population, which makes the data analysis more complex and challenging. No adequate strategy exists for ITT analyses of longitudinal controlled clinical trial data with missing values. Several ad hoc strategies for dealing with missing values for an ITT analysis are common in the practice of controlled clinical trials. We performed a detailed investigation based on simulation studies to develop recommendations for this situation. We compared sizes (type I errors) and power between some popular ad hoc approaches and the linear mixed model approach under different missing value scenarios. Our results suggest that, for studies with a high percentage of missing values, the mixed model approach without any ad hoc imputation is more powerful than other options.
Preventing And Treating Missing Data In Longitudinal Clinical Trials
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Author : Craig H. Mallinckrodt
language : en
Publisher: Cambridge University Press
Release Date : 2013-01-28
Preventing And Treating Missing Data In Longitudinal Clinical Trials written by Craig H. Mallinckrodt and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-01-28 with Mathematics categories.
Focuses on the prevention and treatment of missing data in longitudinal clinical trials, looking at key principles and explaining analytic methods.
Missing Data In Clinical Studies
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Author : Geert Molenberghs
language : en
Publisher: John Wiley & Sons
Release Date : 2007-04-04
Missing Data In Clinical Studies written by Geert Molenberghs and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-04-04 with Medical categories.
Missing 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. The authors focus on practical and modeling concepts, providing an extensive set of case studies to illustrate the problems described. Provides a practical guide to the analysis of clinical trials and related studies with missing data. Examines the problems caused by missing data, enabling a complete understanding of how to overcome them. Presents conventional, simple methods to tackle these problems, before addressing more advanced approaches, including sensitivity analysis, and the MAR missingness mechanism. Illustrated throughout with real-life case studies and worked examples from clinical trials. Details the use and implementation of the necessary statistical software, primarily SAS. Missing Data in Clinical Studies has been developed through a series of courses and lectures. Its practical approach will appeal to applied statisticians and biomedical researchers, in particular those in the biopharmaceutical industry, medical and public health organisations. Graduate students of biostatistics will also find much of benefit.