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More About This Title Regression Methods for Medical Research
English
Regression Methods for Medical Research provides medical researchers with the skills they need to critically read and interpret research using more advanced statistical methods. The statistical requirements of interpreting and publishing in medical journals, together with rapid changes in science and technology, increasingly demands an understanding of more complex and sophisticated analytic procedures.
The text explains the application of statistical models to a wide variety of practical medical investigative studies and clinical trials. Regression methods are used to appropriately answer the key design questions posed and in so doing take due account of any effects of potentially influencing co-variables. It begins with a revision of basic statistical concepts, followed by a gentle introduction to the principles of statistical modelling. The various methods of modelling are covered in a non-technical manner so that the principles can be more easily applied in everyday practice. A chapter contrasting regression modelling with a regression tree approach is included. The emphasis is on the understanding and the application of concepts and methods. Data drawn from published studies are used to exemplify statistical concepts throughout.
Regression Methods for Medical Research is especially designed for clinicians, public health and environmental health professionals, para-medical research professionals, scientists, laboratory-based researchers and students.
English
Bee-Choo Tai, Saw Swee Hock School of Public Health, National University of Singapore, and National University Health System; and Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore
David Machin, Medical Statistics Unit, School of Health and Related Sciences, University of Sheffield; and Cancer Studies, Faculty of Medicine, University of Leicester, UK
English
Preface viii
1 Introduction 1
2 Linear regression: practical issues 25
3 Multiple linear regression 43
4 Logistic Regression 64
5 P oisson Regression 98
6 Time-to-Event Regression 120
7 Model Building 146
8 Repeated Measures 176
9 Regression Trees 204
10 Further Time-to-Event Models 236
11 Further Topics 269
Statistical Tables 285
References 294
Index 298
