Craig Mallinckrodt, Ilya Lipkovich

Analyzing Longitudinal Clinical Trial Data (eBook, ePUB)

A Practical Guide

• Format: ePub

Produktbeschreibung

Analyzing 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 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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Produktdetails

• Verlag: Taylor & Francis eBooks
• Seitenzahl: 336
• Erscheinungstermin: 12. Dezember 2016
• Englisch
• ISBN-13: 9781351737685
• Artikelnr.: 47510061

Autorenporträt

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.

Inhaltsangabe

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.