Applied Surrogate Endpoint Evaluation Methods with SAS and R (Chapman & Hall/CRC Biostatistics Series)

An important factor that affects the duration, complexity and cost of a clinical trial is the endpoint used to study the treatment’s efficacy. When a true endpoint is difficult to use because of such factors as long follow-up times or prohibitive cost, it is sometimes possible to use a surrogate endpoint that can be measured in a more convenient or cost-effective way. This book focuses on the use of surrogate endpoint evaluation methods in practice, using SAS and R.

Science & Math

Burzykowski, Tomasz

Shkedy, Ziv

Molenberghs, Geert

Alonso, Ariel

Bigirumurame, Theophile

Buyse, Marc

Muchene, Leacky

Perualila, Nolen Joy

Van der Elst, Wim

Biological Sciences

Chapman and Hall/CRC

Models for Discrete Longitudinal Data (Springer Series in Statistics)

The linear mixed model has become the main parametric tool for the analysis of continuous longitudinal data, as the authors discussed in their 2000 book. Without putting too much emphasis on software, the book shows how the different approaches can be implemented within the SAS software package. The authors received the American Statistical Association's Excellence in Continuing Education Award based on short courses on longitudinal and incomplete data at the Joint Statistical Meetings of 2002 and 2004.

Verbeke, Geert

Springer

This book provides a comprehensive treatment of linear mixed models for continuous longitudinal data. Next to model formulation, this edition puts major emphasis on exploratory data analysis for all aspects of the model, such as the marginal model, subject-specific profiles, and residual covariance structure. Further, model diagnostics and missing data receive extensive treatment. Sensitivity analysis for incomplete data is given a prominent place. Most analyses were done with the MIXED procedure of the SAS software package, but the data analyses are presented in a software-independent fashion.

Linear Mixed Models for Longitudinal Data (Springer Series in Statistics)

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. <ul>
<li>Provides a practical guide to the analysis of clinical trials and related studies with missing data.</li> <li>Examines the problems caused by missing data, enabling a complete understanding of how to overcome them.</li> <li>Presents conventional, simple methods to tackle these problems, before addressing more advanced approaches, including sensitivity analysis, and the MAR missingness mechanism.</li> <li>Illustrated throughout with real-life case studies and worked examples from clinical trials.</li> <li>Details the use and implementation of the necessary statistical software, primarily SAS.</li> </ul>
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.

Missing Data in Clinical Studies

Kenward, Michael

wiley

Estimands, Estimators and Sensitivity Analysis in Clinical Trials (Chapman & Hall/CRC Biostatistics Series)

The concepts of estimands, analyses (estimators), and sensitivity are interrelated. Therefore, great need exists for an integrated approach to these topics. This book acts as a practical guide to developing and implementing statistical analysis plans by explaining fundamental concepts using accessible language, providing technical details, real-world examples, and SAS and R code to implement analyses. The updated ICH guideline raises new analytic and cross-functional challenges for statisticians. Gaps between different communities have come to surface, such as between causal inference and clinical trialists, as well as among clinicians, statisticians, and regulators when it comes to communicating decision-making objectives, assumptions, and interpretations of evidence. 
This book lays out a path toward bridging some of these gaps. It offers 
¿ A common language and unifying framework along with the technical details and practical guidance to help statisticians meet the challenges 
¿ A thorough treatment of intercurrent events (ICEs), i.e., postrandomization events that confound interpretation of outcomes and five strategies for ICEs in ICH E9 (R1) 
¿ Details on how estimands, integrated into a principled study development process, lay a foundation for coherent specification of trial design, conduct, and analysis needed to overcome the issues caused by ICEs: 
¿ A perspective on the role of the intention-to-treat principle 
¿ Examples and case studies from various areas 
¿ Example code in SAS and R 
¿ A connection with causal inference 
¿ Implications and methods for analysis of longitudinal trials with missing data 
Together, the authors have offered the readers their ample expertise in clinical trial design and analysis, from an industrial and academic perspective.

Ratitch, Bohdana

Mallinckrodt, Craig

Lipkovich, Ilya

Reference

The concepts of estimands, analyses (estimators), and sensitivity are interrelated. Therefore, great need exists for an integrated approach to these topics. This book acts as a practical guide to developing and implementing statistical analysis plans by explaining fundamental concepts using accessible language, providing technical details, real-world examples, and SAS and R code to implement analyses. The updated ICH guideline raises new analytic and cross-functional challenges for statisticians. Gaps between different communities have come to surface, such as between causal inference and clinical trialists, as well as among clinicians, statisticians, and regulators when it comes to communicating decision-making objectives, assumptions, and interpretations of evidence. This book lays out a path toward bridging some of these gaps. It offers A common language and unifying framework along with the technical details and practical guidance to help statisticians meet the challenges A thorough treatment of intercurrent events (ICEs), i.e., postrandomization events that confound interpretation of outcomes and five strategies for ICEs in ICH E9 (R1) Details on how estimands, integrated into a principled study development process, lay a foundation for coherent specification of trial design, conduct, and analysis needed to overcome the issues caused by ICEs: A perspective on the role of the intention-to-treat principle Examples and case studies from various areas Example code in SAS and R A connection with causal inference Implications and methods for analysis of longitudinal trials with missing data Together, the authors have offered the readers their ample expertise in clinical trial design and analysis, from an industrial and academic perspective.

This paperback edition is a reprint of the 2000 edition. It provides comprehensive coverage of linear mixed models for continuous longitudinal data. Next to model formulation, it puts major emphasis on exploratory data analysis for all aspects of the model.

Mathematics

The linear mixed model has become the main parametric tool for the analysis of continuous longitudinal data, as the authors discussed in their 2000 book. Without putting too much emphasis on software, the book shows how the different approaches can be implemented within the SAS software package. The authors received the American Statistical Association's Excellence in Continuing Education Award based on short courses on longitudinal and incomplete data at the Joint Statistical Meetings of 2002 and 2004.

This book provides a comprehensive treatment of linear mixed models for continuous longitudinal data. Next to model formulation, this edition puts major emphasis on exploratory data analysis for all aspects of the model, such as the marginal model, subject-specific profiles, and residual covariance structure. Further, model diagnostics and missing data receive extensive treatment. Sensitivity analysis for incomplete data is given a prominent place.Most analyses were done with the MIXED procedure of the SAS software package, but the data analyses are presented in a software-independent fashion.

See the best price to sell, buy, or rent books by Molenberghs, Geert. BookScouter helps to compare book prices from 25+ online bookstores and 30+ buyback vendors with a single search.

Books by Molenberghs, Geert

See the best price to sell, buy, or rent books by Molenberghs, Geert. BookScouter helps to compare book prices from 25+ online bookstores and 30+ buyback vendors with a single search.

Applied Surrogate Endpoint Evaluation Methods with SAS and R (Chapman & Hall/CRC Biostatistics Series)

Models for Discrete Longitudinal Data (Springer Series in Statistics)

Linear Mixed Models for Longitudinal Data (Springer Series in Statistics)

Missing Data in Clinical Studies

Estimands, Estimators and Sensitivity Analysis in Clinical Trials (Chapman & Hall/CRC Biostatistics Series)

Estimands, Estimators and Sensitivity Analysis in Clinical Trials (Chapman & Hall/CRC Biostatistics Series)

Linear Mixed Models for Longitudinal Data (Springer Series in Statistics)

Models for Discrete Longitudinal Data (Springer Series in Statistics)

Linear Mixed Models for Longitudinal Data (Springer Series in Statistics)

Applied Surrogate Endpoint Evaluation Methods with SAS and R (Chapman & Hall/CRC Biostatistics Series)