Description
Book SynopsisAnalyzing Longitudinal Clinical Trial Data: A Practical Guide provides practical and easy to implement approaches for bringing the latest theory on analysis of longitudinal clinical trial data into routine practice.The 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 s
Trade Review
"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
Table of ContentsBackground 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.
