Main description:

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 writing this 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".

Contents:

Preface

Chapter 1.

Continuous Outcome Regressions

Chapter 2.

Dichotomous Outcome Regressions

Chapter 3.

Confirmative Regressions

Chapter 4.

Dichotomous Regressions Other than Logistic and Cox

Chapter 5.

Polytomous Outcome Regressions

Chapter 6.

Time to Event Regressions other than Traditional Cox

Chapter 7.

Analysis of Variance (ANOVA)

Chapter 8.

Repeated Outcome Regressions

Chapter 9.

Methodologies for Better Fit of Categorical Predictors

Chapter 10.

Laplace Regressions, Multi- instead of Mono-Exponential Models

Chapter 11.

Regressions For Making Extrapolations.

Chapter 12.

Standardized Regression Coefficients

Chapter 13.

Multivariate Analysis of Variance and Canonical Regression

Chapter 14.

More on Poisson Regressions

Chapter 15.

Regression Trend Testing

Chapter 16.

Optimal Scaling and Automatic Linear Regression

Chapter 17.

Spline Regressions

Chapter 18.

More on Nonlinear Regressions

Chapter 19.

Special Forms of Continuous Outcome Regressions

Chapter 20.

Regressions for Quantitative Diagnostic Testing

Chapter 21.

Regressions, a Panacee or at Least a Widespread Help for Data Analyses

Chapter 22.

Regression Trees

Chapter 23.

Regressions with Latent Variables

Chapter 24.

Partial Correlations

Chapter 25.

Functional Data Analysis I

Chapter 26.

Functional Data Analysis II

Index