Analyzing Longitudinal Clinical Trial Data: A Practical Guide

Craig Mallinckrodt, Ilya Lipkovich



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.

Features

• Emphasizes how to analyze longitudinal data that may include missing data

• Focuses on the most relevant and current theory for the common issues faced in planning and implementing analyses for longitudinal trials

• Emphasizes bringing that theory into routine use in a practical and efficient manner via extensive examples with realistic data and the programming code to implement the analyses.

• Uses a holistic approach that considers the interactions between estimands (what is to be estimated), trial design, and trial analyses, along with the focus on practical implementation that sets this text apart from existing texts.



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