Analyzing Longitudinal Clinical Trial Data: A Practical Guide: Chapman & Hall/CRC Biostatistics Series
Autor Craig Mallinckrodt, Ilya Lipkovichen Limba Engleză Paperback – 18 dec 2020
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.
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Specificații
ISBN-13: 9780367736583
ISBN-10: 0367736586
Pagini: 336
Dimensiuni: 156 x 234 x 25 mm
Greutate: 0.61 kg
Ediția:1
Editura: CRC Press
Colecția Chapman and Hall/CRC
Seria Chapman & Hall/CRC Biostatistics Series
ISBN-10: 0367736586
Pagini: 336
Dimensiuni: 156 x 234 x 25 mm
Greutate: 0.61 kg
Ediția:1
Editura: CRC Press
Colecția Chapman and Hall/CRC
Seria Chapman & Hall/CRC Biostatistics Series
Cuprins
Background and Setting. Introduction. Objectives and estimands–determining what to estimate. Study design–collecting the intended data. Example data. Mixed effects models review.
Modeling the observed data. Choice of dependent variable and statistical test. modeling covariance (correlation). Modeling means over time. Accounting for covariates. Categorical data. Model checking and verification.
Methods for dealing with missing Data. Overview of missing data. Simple and ad hoc Approaches for dealing with missing data. Direct maximum likelihood. Multiple imputation. Inverse probability. Methods for incomplete categorical data weighted generalized estimated equations. Doubly robust methods. MNAR methods. Methods for incomplete categorical data.
A comprehensive approach to study development and analyses. Developing statistical analysis plans. Example analyses of clinical trial data.
Modeling the observed data. Choice of dependent variable and statistical test. modeling covariance (correlation). Modeling means over time. Accounting for covariates. Categorical data. Model checking and verification.
Methods for dealing with missing Data. Overview of missing data. Simple and ad hoc Approaches for dealing with missing data. Direct maximum likelihood. Multiple imputation. Inverse probability. Methods for incomplete categorical data weighted generalized estimated equations. Doubly robust methods. MNAR methods. Methods for incomplete categorical data.
A comprehensive approach to study development and analyses. Developing statistical analysis plans. Example analyses of clinical trial data.
Notă biografică
Craig Mallinckrodt and Ilya Lipkovich each have extensive experience in medical research and longitudinal analyses. Dr. Mallinckrodt is a Research Fellow at Eli Lilly and Company and a Fellow of the American Statistical Association. He has won numerous awards, including the 2014 award for statistical excellence in the Pharmaceutical Industry from the Royal Statistical Society and PSI (Statisticians in the Pharmaceutical Industry). Dr. Lipkovich is a Principal Scientific Advisor at Quintiles. He is a widely-published author and frequent presenter at conferences and has developed a number of successful short courses and tutorials.
Recenzii
"This book deals mostly with longitudinal clinical trial data, but also with the related issue of imputing missing data. The book is an excellent resource overall, as it is fairly comprehensive, well referenced, and clear."
~Vance W. Berger, PhD, NIH/NCI/DCP/BRG
Analyzing Longitudinal Clinical Trial Data provides, in a well organized and small format, a fairly easy read that could be helpful for both researchers analyzing longitudinal data collected from clinical trials (or perhaps even observational studies) and instructors teaching undergraduate and graduate courses on clinical trials, longitudinal data, and missing data. The book is divided into four well-structured and complementary sections: background and setting, general modeling strategies and methods, methods for dealing with missing data, and overall guidance (with illustration) for developing a study.
~Journal of the American Statistical Association
"I recommend this book to anyone who deals with longitudinal clinical trials data at any level with confidence as it concisely presents essential ideas and analysis techniques with illustrative examples, in an intuitively appealing way, both on analytic and conceptual levels. It addresses an important need for practicing (bio)statisticians."
~Biometrical Journal
~Vance W. Berger, PhD, NIH/NCI/DCP/BRG
Analyzing Longitudinal Clinical Trial Data provides, in a well organized and small format, a fairly easy read that could be helpful for both researchers analyzing longitudinal data collected from clinical trials (or perhaps even observational studies) and instructors teaching undergraduate and graduate courses on clinical trials, longitudinal data, and missing data. The book is divided into four well-structured and complementary sections: background and setting, general modeling strategies and methods, methods for dealing with missing data, and overall guidance (with illustration) for developing a study.
~Journal of the American Statistical Association
"I recommend this book to anyone who deals with longitudinal clinical trials data at any level with confidence as it concisely presents essential ideas and analysis techniques with illustrative examples, in an intuitively appealing way, both on analytic and conceptual levels. It addresses an important need for practicing (bio)statisticians."
~Biometrical Journal
Descriere
Missing data in longitudinal clinical trials has justifiably been the target of considerable research. However, missing data is just one of the many considerations in the analysis of longitudinal data, and focus on the data we don’t have should not distract from focus on the data we do have. The statistical theory relevant to analyses of longitu