Regression Analysis in Medical Research: for Starters and 2nd Levelers

Front Cover
Springer, Jan 29, 2018 - Science - 426 pages

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

Continuous Outcome Regressions
1
Dichotomous Outcome Regressions
41
Confirmative Regressions
60
Dichotomous Regressions Other Than Logistic and Cox
75
Polytomous Outcome Regressions
104
Time to Event Regressions Other Than Traditional Cox
131
Analysis of Variance
147
Repeated Outcomes Regression Methods
154
Optimal Scaling and Automatic Linear Regression
254
Spline Regression Modeling
267
More on Nonlinear Regressions
279
Special Forms of Continuous Outcomes Regressions
299
Regressions for Quantitative Diagnostic Testing
319
Regressions a Panacee or at Least a Widespread Help for Data Analyses
327
Regression Trees
359
Regressions with Latent Variables
365

Methodologies for Better Fit of Categorical Predictors
179
Laplace Regressions Multiinstead of Monoexponential Regressions
193
Regressions for Making Extrapolations
201
Standardized Regression Coefficients
213
Multivariate Analysis of Variance and Canonical Regression
226
More on Poisson Regressions
237
Regression Trend Testing
249
Partial Correlations
387
Functional Data Analysis I
393
Functional Data Analysis II
407
References
416
Index
423
Copyright

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About the author (2018)

The authors are well-qualified in their field. Professor Zwinderman is past-president of the International Society of Biostatistics (2012-2015), and Professor Cleophas is past-president of the American College of Angiology (2000-2002).
Professor Zwinderman is one of the Principle Investigators of the Academic Medical Center Amsterdam, and his research is concerned with developing statistical methods for new research designs in biomedical science, particularly integrating omics data, like genomics, proteomics, metabolomics, and analysis tools based on parallel computing and the use of cluster computers and grid computing.
Professor Cleophas is a member of the Academic Committee of the European College of Pharmaceutical Medicine, that provides, on behalf of 22 European Universities, the Master-ship trainings "Pharmaceutical Medicine" and "Medicines Development".
From their expertise they should be able to make adequate selections of modern methods for clinical data analysis for the benefit of physicians, students, and investigators. The authors have been working and publishing together for 18 years, and their research can be characterized as a continued effort to demonstrate that clinical data analysis is not mathematics but rather a discipline at the interface of biology and mathematics.
The authors as professors and teachers in statistics at universities in The Netherlands and France for the most part of their lives, are concerned, 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, and they do hope that the current production "Regression Analysis for Starters and 2nd Levelers" will be a helpful companion for the purpose. Five textbooks complementary to the current production and written by the same authors are
Statistics applied to clinical studies 5th edition, 2012, Machine learning in medicine a complete overview, 2015, SPSS for starters and 2nd levelers 2nd edition, 2015, Clinical data analysis on a pocket calculator 2nd edition, 2016, Modern Meta-analysis, 2017, all of them published by Springer

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