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Applied mixed models in medicine / Helen Brown and Robin Prescott.

By: Contributor(s): Material type: TextTextSeries: Statistics in practicePublication details: Chichester : John Wiley, 2015.Edition: 3rd edDescription: xix, 516 p. ; illISBN:
  • 9781118778258 (cloth)
Subject(s): Additional physical formats: Online version:: Applied mixed models in medicineDDC classification:
  • 000SB:610 23 B878
Contents:
1. Introduction-- 2. Normal mixed models-- 3. Generalised linear mixed models-- 4. Mixed models for categorical data-- 5. Multi-centre trials and meta-analyses-- 6. Repeated measures data-- 7. Cross-over trials-- 8. Other applications of mixed models-- 9. Software for fitting mixed models-- Glossary-- References-- Index.
Summary: 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.
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Holdings
Item type Current library Call number Status Date due Barcode Item holds
Books ISI Library, Kolkata 000SB:610 B878 (Browse shelf(Opens below)) Available 135819
Total holds: 0

Includes bibliographical references and index.

1. Introduction--
2. Normal mixed models--
3. Generalised linear mixed models--
4. Mixed models for categorical data--
5. Multi-centre trials and meta-analyses--
6. Repeated measures data--
7. Cross-over trials--
8. Other applications of mixed models--
9. Software for fitting mixed models--
Glossary--
References--
Index.

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.

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