Regression Analysis In Medical Research For Starters And 2nd Levelers 1st edition by Ton J. Cleophas, Aeilko H. Zwinderman – Ebook PDF Instant Download/Delivery: 3030613941, 978-3030613945
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ISBN 10: 3030613941
ISBN 13: 978-3030613945
Author: Ton J. Cleophas, Aeilko H. Zwinderman
This edition is a pretty complete textbook and tutorial for medical and health care students, as well as a recollection/update bench, and help desk for professionals. Novel approaches already applied in published clinical research will be addressed: matrix analyses, alpha spending, gate keeping, kriging, interval censored regressions, causality regressions, canonical regressions, quasi-likelihood regressions, novel non-parametric regressions. Each chapter can be studied as a stand-alone, and covers one field in the fast growing world of regression analyses.
The authors, as professors in statistics and machine learning at European universities, are worried, that their students find regression-analyses harder than any other methodology in statistics. This is serious, because almost all of the novel methodologies in current data mining and data analysis include elements of regression-analysis. It is the main incentive for writingthis 28 chapter edition, consistent of
– 28 major fields of regression analysis,
– their condensed maths,
– their applications in medical and health research as published so far,
– step by step analyses for self-assessment,
– conclusion and reference sections.
Traditional regression analysis is adequate for epidemiology, but lacks the precision required for clinical investigations. However, in the past two decades modern regression methods have proven to be much more precise.And so it is time, that a book described regression analyses for clinicians. The current edition is the first to do so. It is written for a non-mathematical readership. Self-assessment data-files are provided through Springer’ s “Extras Online”.
Regression Analysis In Medical Research For Starters And 2nd Levelers 1st Table of contents:
1. Continuous Outcome Regressions
2. Dichotomous Outcome Regressions
3. Confirmative Regressions
4. Dichotomous Regressions Other than Logistic and Cox
5. Polytomous Outcome Regressions
6. Time to Event Regressions Other than Traditional Cox
7. Analysis of Variance (Anova)
8. Repeated Outcomes Regression Methods
9. Methodologies for Better Fit of Categorical Predictors
10. Laplace Regressions, Multi- Instead of Mono-Exponential Regressions
11. Regressions for Making Extrapolations
12. Standardized Regression Coefficients (SEMs)
13. Multivariate Analysis of Variance and Canonical Regression
14. More on Poisson Regressions
15. Regression Trend Testing
16. Optimal Scaling and Automatic Linear Regression
17. Spline Regression Modeling
18. More on Nonlinear Regressions
19. Special Forms of Continuous Outcomes Regressions
20. Regressions for Quantitative Diagnostic Testing
21. Regressions, a Panacee or at Least a Widespread Help for Clinical Data Analyses
22. Regression Trees
23. Regressions with Latent Variables
24. Partial Correlations
25. Functional Data Analysis (FDA) Basis
26. Functional Data Analysis (FDA) Advanced
27. Quantile Regression
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