Basic Principles of Applied Medical Statistics
Description
This book describes the basic principles of medical statistics. Although there are many texts on this topic, this one is different in that it limits the mathematics to an absolute minimum. It will therefore be extremely useful for non-mathematical researchers who need to use statistical methods in their work. Here, they will learn how to choose the appropriate statistical method and how to interpret the results obtained from statistical software. An important feature of the book is that it highlights the common thread throughout the statistical methods. By understanding this common thread, the analyses become much easier to comprehend. Each chapter contains multiple examples, and because the example datasets are available, the readers can reanalyze them and practice how to perform a particular statistical analysis.
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Person
Jos W. R. Twisk is a professor in applied biostatistics at the department of Epidemiology and Data Science of the Amsterdam University Medical Centers (AUMC). He studied human movement science in Amsterdam, and after his graduation in 1990, he joined the research team of the Amsterdam Growth and Health Longitudinal Study. After his PhD, which was related to this longitudinal study, he supervised several projects within the AGHSL. In 2005, he was appointed as a full professor of applied biostatistics. His main activities nowadays involve teaching (at several levels) and providing consultancies to researchers from the AUMC and other universities from inside and outside the Netherlands. He has written several textbooks on longitudinal data analysis, mixed model analysis and the analysis of randomized controlled trial data. He also conducts applied methodological research and is the (co)author of more than 1000 peer reviewed scientific papers.
Content
- 1. Introduction.- 2. Descriptive Statistics.- 3. Basic Principles of Explanatory Statistics.- 4. The Analysis of a Continuous Outcome Variable.- 5. The Analysis of a Dichotomous Outcome Variable.- 6. The Analysis of Survival Data.- 7. Multiple Regression Analysis - Association Models and Prediction Models.- 8. Machine Learning.- 9. Other Outcome Variables.- 10. Missing Data.- 11. Pitfalls in (Multiple) Regression Analysis.- 12. Miscellaneous.- 13. Overview.- 14. Example Datasets.
