Analyzing Longitudinal Clinical Trial Data: A Practical Guide

✍ Scribed by Lipkovich, Ilya; Mallinckrodt, Craig H

• Publisher: Chapman and Hall/CRC
• Year: 2017
• Tongue: English
• Leaves: 330
• Series: Chapman & Hall/CRC Biostatistics Series
• Category: Library

✦ Synopsis

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.

✦ Table of Contents

Content: 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.

✦ Subjects

Clinical trials;Longitudinal studies.;Clinical trials.;MEDICAL / Pharmacology;Clinical Trials as Topic;statistics and numerical data.