Applied Mixed Models in Medicine
Helen Brown, Robin Prescott
- English
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Applied Mixed Models in Medicine
Helen Brown, Robin Prescott
Über dieses Buch
A fully updated edition of this key text on mixed models, focusing on applications in medical research
The application of mixed models is an increasingly popular way of analysing medical data, particularly in the pharmaceutical industry. A mixed model allows the incorporation of both fixed and random variables within a statistical analysis, enabling efficient inferences and more information to be gained from the data. There have been many recent advances in mixed modelling, particularly regarding the software and applications. This third edition of Brown and Prescott's groundbreaking text provides an update on the latest developments, and includes guidance on the use of current SAS techniques across a wide range of applications.
- Presents an overview of the theory and applications of mixed models in medical research, including the latest developments and new sections on incomplete block designs and the analysis of bilateral data.
- Easily accessible to practitioners in any area where mixed models are used, including medical statisticians and economists.
- Includes numerous examples using real data from medical and health research, and epidemiology, illustrated with SAS code and output.
- Features the new version of SAS, including new graphics for model diagnostics and the procedure PROC MCMC.
- Supported by a website featuring computer code, data sets, and further material.
This third edition will appeal to applied statisticians working in medical research and the pharmaceutical industry, as well as teachers and students of statistics courses in mixed models. The book will also be of great value to a broad range of scientists, particularly those working in the medical and pharmaceutical areas.
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Information
Chapter 1
Introduction
1.1 The use of mixed models
- Example 1: Utilisation of incomplete information in a cross-over trial Cross-over trials are often utilised to assess treatment efficacy in chronic conditions, such as asthma. In such conditions, an individual patient can be tested for response to a succession of two or more treatments, giving the benefit of a ‘within-patient’ comparison. In the most commonly used cross-over design, just two treatments are compared. If, for generality, we call these treatments A and B, then patients will be assessed either on their response to treatment A, followed by their response to treatment B, or vice versa. If all patients complete the trial, and both treatments are assessed, then the analysis is fairly straightforward. However, commonly, patients drop out during the trial and may have a valid observation from only the first treatment period. These incomplete observations cannot be utilised in a conventional analysis. In contrast, the use of a mixed model will allow all of the observations to be analysed, resulting in more accurate comparisons of the efficacy of treatment. This benefit, of more efficient use of the data, applies to all types of cross-over trial where there are missing data.
- Example 2: Cross-over trials with fewer treatment periods than treatments In cross-over trials, for logistical reasons, it may be impractical to ask a patient to evaluate more than two treatments (e.g. if the treatment has to be given for several weeks). Nevertheless, there may be the need to evaluate three or more treatments. Special types of cross-over design can be used in this situation, but a simple analysis will be very inefficient. Mixed models provide a straightforward method of analysis, which fully uses the data, resulting again in more precise estimates of the effect of the treatments.
- Example 3: A surgical audit A surgical audit is to be carried out to investigate how different hospitals compare in their rates of postoperative complications following a particular operation. As some hospitals carry out the operation commonly, while other hospitals perform the operation rarely, the accuracy with which the complication rates are estimated will va...