Craig Mallinckrodt, Ilya Lipkovich

Analyzing Longitudinal Clinical Trial Data (eBook, PDF)

A Practical Guide

• Format: PDF

Produktbeschreibung

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 longitudinal data is extensive. However, the greatest difficulty may not be in knowing the theory, but rather in putting the theory into useful practice. Therefore, this book focuses on the most relevant and current theory for the common issues faced in planning and implementing analyses for longitudinal trials.

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Produktdetails

• Verlag: Taylor & Francis
• Seitenzahl: 336
• Erscheinungstermin: 12. Dezember 2016
• Englisch
• ISBN-13: 9781351737692
• Artikelnr.: 47392282

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.