Applied mixed model analysis : a practical guide / Jos W. R. Twisk.
By: Twisk, Jos W. R [author.].
Series: Practical guides to biostatistics and epidemiology: Publisher: Cambridge : Cambridge University Press, 2019Edition: Second edition.Description: 1 online resource (xii, 235 pages) : digital, PDF file(s).Content type: text Media type: computer Carrier type: online resourceISBN: 9781108635660 .Subject(s): Medical statistics | Analysis of variance | Mathematical statisticsGenre/Form: Electronic booksAdditional physical formats: Print version: : No titleDDC classification: 519.538 Online resources: Click here to access online Summary: This practical book is designed for applied researchers who want to use mixed models with their data. It discusses the basic principles of mixed model analysis, including two-level and three-level structures, and covers continuous outcome variables, dichotomous outcome variables, and categorical and survival outcome variables. Emphasizing interpretation of results, the book develops the most important applications of mixed models, such as the study of group differences, longitudinal data analysis, multivariate mixed model analysis, IPD meta-analysis, and mixed model predictions. All examples are analyzed with STATA, and an extensive overview and comparison of alternative software packages is provided. All datasets used in the book are available for download, so readers can re-analyze the examples to gain a strong understanding of the methods. Although most examples are taken from epidemiological and clinical studies, this book is also highly recommended for researchers working in other fields.| Item type | Current location | Call number | Status | Date due | Barcode |
|---|---|---|---|---|---|
| Taylor's Library-TU | On order |
Title from publisher's bibliographic system (viewed on 15 May 2019).
This practical book is designed for applied researchers who want to use mixed models with their data. It discusses the basic principles of mixed model analysis, including two-level and three-level structures, and covers continuous outcome variables, dichotomous outcome variables, and categorical and survival outcome variables. Emphasizing interpretation of results, the book develops the most important applications of mixed models, such as the study of group differences, longitudinal data analysis, multivariate mixed model analysis, IPD meta-analysis, and mixed model predictions. All examples are analyzed with STATA, and an extensive overview and comparison of alternative software packages is provided. All datasets used in the book are available for download, so readers can re-analyze the examples to gain a strong understanding of the methods. Although most examples are taken from epidemiological and clinical studies, this book is also highly recommended for researchers working in other fields.