Aeilko H. Zwinderman – författare

In contrast, the adverse effect "increased calorie intake" occurs with "increased exercise", and this adverse effect is very dependent on the main outcome "weight loss".

The current textbook has been written as a help to medical / health professionals and students for the study of modern Bayesian statistics, where posterior and prior odds have been replaced with posterior and prior likelihood distributions.

Machine learning and big data is hot. It is, however, virtually unused in clinical trials. This is so, because randomization is applied to even out multiple variablesModern medical computer files often involve hundreds of variables like genes and other laboratory values, and computationally intensive methods are requiredThis is the first publication of clinical trials that have been systematically analyzed with machine learning. In addition, all of the machine learning analyses were tested against traditional analyses. Step by step statistics for self-assessments are includedThe authors conclude, that machine learning is often more informative, and provides better sensitivities of testing than traditional analytic methods do

Step by step statistics for self-assessments are includedThe authors conclude, that machine learning is often more informative, and provides better sensitivities of testing than traditional analytic methods do

Adequate health and health care is no longer possible without proper data supervision from modern machine learning methodologies like cluster models, neural networks, and other data mining methodologies. The current book is the first publication of a complete overview of machine learning methodologies for the medical and health sector, and it was written as a training companion, and as a must-read, not only for physicians and students, but also for any one involved in the process and progress of health and health care.In this second edition the authors have removed the textual errors from the first edition. Also, the improved tables from the first edition, have been replaced with the  original tables from the software programs as applied. This is, because, unlike the former, the latter were without error, and readers were better familiar with them.The main purpose of the first edition was, to provide stepwise analyses of the novel  methods from data examples, but background information and clinical relevance information may have been somewhat lacking. Therefore, each chapter now contains a section entitled "Background Information".Machine learning may be more informative, and may provide better sensitivity of testing than traditional analytic methods may do. In the second edition a place has been given for the use of machine learning not only to the analysis of observational clinical data, but also to that of controlled clinical trials.Unlike the first edition, the second edition has drawings in full color providing a helpful extra dimension to the data analysis.Several machine learning methodologies not yet covered in the first edition, but increasingly important today, have been included in this updated edition, for example, negative binomial and Poisson regressions, sparse canonical analysis, Firth's bias adjusted logistic analysis, omics research, eigenvalues and eigenvectors.

Adequate health and health care is no longer possible without proper data supervision from modern machine learning methodologies like cluster models, neural networks, and other data mining methodologies. The current book is the first publication of a complete overview of machine learning methodologies for the medical and health sector, and it was written as a training companion, and as a must-read, not only for physicians and students, but also for any one involved in the process and progress of health and health care.In this second edition the authors have removed the textual errors from the first edition. Also, the improved tables from the first edition, have been replaced with the  original tables from the software programs as applied. This is, because, unlike the former, the latter were without error, and readers were better familiar with them.The main purpose of the first edition was, to provide stepwise analyses of the novel  methods from data examples, but background information and clinical relevance information may have been somewhat lacking. Therefore, each chapter now contains a section entitled "Background Information".Machine learning may be more informative, and may provide better sensitivity of testing than traditional analytic methods may do. In the second edition a place has been given for the use of machine learning not only to the analysis of observational clinical data, but also to that of controlled clinical trials.Unlike the first edition, the second edition has drawings in full color providing a helpful extra dimension to the data analysis.Several machine learning methodologies not yet covered in the first edition, but increasingly important today, have been included in this updated edition, for example, negative binomial and Poisson regressions, sparse canonical analysis, Firth's bias adjusted logistic analysis, omics research, eigenvalues and eigenvectors.

Therefore, the editorial art work has now been systematically replaced with original statistical software tables and graphs for the benefit of an improved usage and understanding of the methods. In the past few years, professionals have been flooded with big data.

Therefore, the editorial art work has now been systematically replaced with original statistical software tables and graphs for the benefit of an improved usage and understanding of the methods. In the past few years, professionals have been flooded with big data.

Quantile regression is an approach to data at a loss of homogeneity, for example (1) data with outliers, (2) skewed data like corona - deaths data, (3) data with inconstant variability, (4) big data. Step by step analyses of over 20 data files stored at extras.springer.com are included for self-assessment.

Quantile regression is an approach to data at a loss of homogeneity, for example (1) data with outliers, (2) skewed data like corona - deaths data, (3) data with inconstant variability, (4) big data. Step by step analyses of over 20 data files stored at extras.springer.com are included for self-assessment.

IBM (international business machines) has published in its SPSS statistical software 2022 update a very important novel regression method entitled Kernel Ridge Regression (KRR). It is an extension of the currently available regression methods, and is suitable for pattern recognition in high dimensional data, particularly, when alternative methods fail. Its theoretical advantages are plenty and include thekernel trick for reduced arithmetic complexity,estimation of uncertainty by Gaussians unlike histograms,corrected data-overfit by ridge regularization,availability of 8 alternative kernel density models for datafit.A very exciting and wide array of preliminary KRR research has already been published by major disciplines (like studies in quantum mechanics and nuclear physics, studies of molecular affinity / dynamics, atomisation energy studies, but also forecasting economics studies, IoT (internet of things)studies for e-networks, plant stress response studies, big data streaming studies, etc). In contrast, it is virtually unused in clinical research. This edition is the first textbook and tutorial of kernel ridge regressions for medical and healthcare students as well as recollection / update bench, and help desk for professionals. Each chapter can be studied as a standalone, and, using, real as well as hypothesized data, it tests the performance of the novel methodology against traditional regression analyses. Step by step analyses of over 20 data files stored at Supplementary Files at Springer Interlink are included for self-assessment. We should add that 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). From their expertise they should be able to make adequate selections of modern KRR methods for the benefit of physicians, students, and investigators. The authors have been working and publishing together for 24 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.

IBM (international business machines) has published in its SPSS statistical software 2022 update a very important novel regression method entitled Kernel Ridge Regression (KRR). It is an extension of the currently available regression methods, and is suitable for pattern recognition in high dimensional data, particularly, when alternative methods fail. Its theoretical advantages are plenty and include thekernel trick for reduced arithmetic complexity,estimation of uncertainty by Gaussians unlike histograms,corrected data-overfit by ridge regularization,availability of 8 alternative kernel density models for datafit.A very exciting and wide array of preliminary KRR research has already been published by major disciplines (like studies in quantum mechanics and nuclear physics, studies of molecular affinity / dynamics, atomisation energy studies, but also forecasting economics studies, IoT (internet of things)studies for e-networks, plant stress response studies, big data streaming studies, etc). In contrast, it is virtually unused in clinical research. This edition is the first textbook and tutorial of kernel ridge regressions for medical and healthcare students as well as recollection / update bench, and help desk for professionals. Each chapter can be studied as a standalone, and, using, real as well as hypothesized data, it tests the performance of the novel methodology against traditional regression analyses. Step by step analyses of over 20 data files stored at Supplementary Files at Springer Interlink are included for self-assessment. We should add that 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). From their expertise they should be able to make adequate selections of modern KRR methods for the benefit of physicians, students, and investigators. The authors have been working and publishing together for 24 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.

An important novel menu for Survival Analysis entitled Accelerated Failure Time (AFT) models has been published by IBM (international Businesss Machines) in its SPSS statistical software update of 2023. Unlike the traditional Cox regressions that work with hazards, which are the ratio of deaths and non-deaths in a sample, it works with risk of death, which is the proportion of deaths in the same sample. The latter approach may provide better sensitivity of testing, but has been seldom applied, because with computers risks are tricky and hazards because they are odds are fine. This was underscored in 1997 by Keiding and colleague statisticians from Copenhagen University who showed better-sensitive goodness of fit and null-hypothesis tests with AFT than with Cox survival tests.So far, a controlled study of a representative sample of clinical Kaplan Meier assessments, where the sensitivity of Cox regression is systematically tested against that of AFT modeling, hasnot been accomplished. This edition is the first textbook and tutorial of AFT modeling both for medical and healthcare students and for professionals. Each chapter can be studied as a standalone, and, using, real as well as hypothesized data, it tests the performance of the novel methodology against traditional Cox regressions. Step by step analyses of over 20 data files stored at Supplementary Files at Springer Interlink are included for self-assessment.We should add that 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). From their expertise they should be able to make adequate selections of modern data analysis methods for the benefit of physicians, students, and investigators. The authors have been working and publishing together for 25 years and their research can be characterized asa continued effort to demonstrate that clinical data analysis is not mathematics but rather a discipline at the interface of biology and mathematics.

An important novel menu for Survival Analysis entitled Accelerated Failure Time (AFT) models has been published by IBM (international Businesss Machines) in its SPSS statistical software update of 2023. Unlike the traditional Cox regressions that work with hazards, which are the ratio of deaths and non-deaths in a sample, it works with risk of death, which is the proportion of deaths in the same sample. The latter approach may provide better sensitivity of testing, but has been seldom applied, because with computers risks are tricky and hazards because they are odds are fine. This was underscored in 1997 by Keiding and colleague statisticians from Copenhagen University who showed better-sensitive goodness of fit and null-hypothesis tests with AFT than with Cox survival tests.So far, a controlled study of a representative sample of clinical Kaplan Meier assessments, where the sensitivity of Cox regression is systematically tested against that of AFT modeling, hasnot been accomplished. This edition is the first textbook and tutorial of AFT modeling both for medical and healthcare students and for professionals. Each chapter can be studied as a standalone, and, using, real as well as hypothesized data, it tests the performance of the novel methodology against traditional Cox regressions. Step by step analyses of over 20 data files stored at Supplementary Files at Springer Interlink are included for self-assessment.We should add that 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). From their expertise they should be able to make adequate selections of modern data analysis methods for the benefit of physicians, students, and investigators. The authors have been working and publishing together for 25 years and their research can be characterized asa continued effort to demonstrate that clinical data analysis is not mathematics but rather a discipline at the interface of biology and mathematics.

This textbook is an important novel menu for multiple variables regression entitled "regularized regression".

The amount of data in medical databases doubles every 20 months, and physicians are at a loss to analyze them. Also, traditional methods of data analysis have difficulty to identify outliers and patterns in big data and data with multiple exposure / outcome variables and analysis-rules for surveys and questionnaires, currently common methods of data collection, are, essentially, missing.Obviously, it is time that medical and health professionals mastered their reluctance to use machine learning and the current 100 page cookbook should be helpful to that aim. It covers in a condensed form the subjects reviewed in the 750 page three volume textbook by the same authors, entitled “Machine Learning in Medicine I-III” (ed. by Springer, Heidelberg, Germany, 2013) and was written as a hand-hold presentation and must-read publication. It was written not only to investigators and students in the fields, but also to jaded clinicians new to the methods and lacking time to read the entire textbooks.General purposes and scientific questions of the methods are only briefly mentioned, but full attention is given to the technical details. The two authors, a statistician and current president of the International Association of Biostatistics and a clinician and past-president of the American College of Angiology, provide plenty of step-by-step analyses from their own research and data files for self-assessment are available at extras.springer.com.From their experience the authors demonstrate that machine learning performs sometimes better than traditional statistics does. Machine learning may have little options for adjusting confounding and interaction, but you can add propensity scores and interaction variables to almost any machine learning method.

The amount of data medical databases doubles every 20 months, and physicians are at a loss to analyze them. Also, traditional data analysis has difficulty to identify outliers and patterns in big data and data with multiple exposure / outcome variables and analysis-rules for surveys and questionnaires, currently common methods of data collection, are, essentially, missing. Consequently, proper data-based health decisions will soon be impossible.Obviously, it is time that medical and health professionals mastered their reluctance to use machine learning methods and this was the main incentive for the authors to complete a series of three textbooks entitled “Machine Learning in Medicine Part One, Two and Three, Springer Heidelberg Germany, 2012-2013", describing in a nonmathematical way over sixty machine learning methodologies, as available in SPSS statistical software and other major software programs. Although well received, it came to our attention that physicians and students often lacked time to read the entire books, and requested a small book, without background information and theoretical discussions and highlighting technical details.For this reason we produced a 100 page cookbook, entitled "Machine Learning in Medicine - Cookbook One", with data examples available at extras.springer.com for self-assessment and with reference to the above textbooks for background information. Already at the completion of this cookbook we came to realize, that many essential methods were not covered. The current volume, entitled "Machine Learning in Medicine - Cookbook Two" is complementary to the first and also intended for providing a more balanced view of the field and thus, as a must-read not only for physicians and students, but also for any one involved in the process and progress of health and health care.Similarly to Machine Learning in Medicine - Cookbook One, the current work will describe stepwise analyses of over twenty machinelearning methods, that are, likewise, based on the three major machine learning methodologies:Cluster methodologies (Chaps. 1-3)Linear methodologies (Chaps. 4-11)Rules methodologies (Chaps. 12-20)In extras.springer.com the data files of the examples are given, as well as XML (Extended Mark up Language), SPS (Syntax) and ZIP (compressed) files for outcome predictions in future patients. In addition to condensed versions of the methods, fully described in the above three textbooks, an introduction is given to SPSS Modeler (SPSS' data mining workbench) in the Chaps. 15, 18, 19, while improved statistical methods like various automated analyses and Monte Carlo simulation models are in the Chaps. 1, 5, 7 and 8.We should emphasize that all of the methods described have been successfully applied in practice by the authors, both of them professors in applied statistics and machine learning at the European Community College of Pharmaceutical Medicine in Lyon France. We recommend the current work not only as a training companion to investigators and students, because of plenty of step by step analyses given, but also as a brief introductory text to jaded clinicians new to the methods. For the latter purpose, background and theoretical information have been replaced with the appropriate references to the above textbooks, while single sections addressing "general purposes", "main scientific questions" and "conclusions" are given in place.Finally, we will demonstrate that modern machine learning performs sometimes better than traditional statistics does. Machine learning may have little options for adjusting confounding and interaction, but you can add propensity scores and interaction variables to almost any machine learning method.

Unique features of the book involve the following.1.This book is the third volume of a three volume series of cookbooks entitled "Machine Learning in Medicine - Cookbooks One, Two, and Three". No other self-assessment works for the medical and health care community covering the field of machine learning have been published to date.2. Each chapter of the book can be studied without the need to consult other chapters, and can, for the readership's convenience, be downloaded from the internet. Self-assessment examples are available at extras.springer.com.3. An adequate command of machine learning methodologies is a requirement for physicians and other health workers, particularly now, because the amount of medical computer data files currently doubles every 20 months, and, because, soon, it will be impossible for them to take proper data-based health decisions without the help of machine learning.4. Given the importance of knowledge of machine learning in the medical and health care community, and the current lack of knowledge of it, the readership will consist of any physician and health worker.5. The book was written in a simple language in order to enhance readability not only for the advanced but also for the novices.6. The book is multipurpose, it is an introduction for ignorant, a primer for the inexperienced, and a self-assessment handbook for the advanced.7. The book, was, particularly, written for jaded physicians and any other health care professionals lacking time to read the entire series of three textbooks.8. Like the other two cookbooks it contains technical descriptions and self-assessment examples of 20 important computer methodologies for medical data analysis, and it, largely, skips the theoretical and mathematical background.9. Information of theoretical and mathematical background of the methods described are displayed in a "notes" section at the end of eachchapter.10.Unlike traditional statistical methods, the machine learning methodologies are able to analyze big data including thousands of cases and hundreds of variables.11. The medical and health care community is little aware of the multidimensional nature of current medical data files, and experimental clinical studies are not helpful to that aim either, because these studies, usually, assume that subgroup characteristics are unimportant, as long as the study is randomized. This is, of course, untrue, because any subgroup characteristic may be vital to an individual at risk.12. To date, except for a three volume introductary series on the subject entitled "Machine Learning in Medicine Part One, Two, and Thee, 2013, Springer Heidelberg Germany" from the same authors, and the current cookbook series, no books on machine learning in medicine have been published.13. Another unique feature of the cookbooks is that it was jointly written by two authors from different disciplines, one being a clinician/clinical pharmacologist, one being a mathematician/biostatistician.14. The authors have also jointly been teaching at universities and institutions throughout Europe and the USA for the past 20 years.15. The authors have managed to cover the field of medical data analysis in a nonmathematical way for the benefit of medical and health workers.16. The authors already successfully published many statistics textbooks and self-assessment books, e.g., the 67 chapter textbook entitled "Statistics Applied to Clinical Studies 5th Edition, 2012, Springer Heidelberg Germany" with downloads of 62,826 copies.17. The current cookbook makes use, in addition to SPSS statistical software, of various free calculators from the internet, as well as the Konstanz Information Miner (Knime), a widely approved free machine learning package, and the free Weka Data Mining package from New Zealand.18. The above software packages with hundreds of nodes, the basic processing units including virtually all of the statistical and data mining methods, can be used not only for data analyses, but also for appropriate data storage.19. The current cookbook shows, particularly, for those with little affinity to value tables, that data mining in the form of a visualization process is very well feasible, and often more revealing than traditional statistics.20.The Knime and Weka data miners uses widely available excel data files.21. In current clinical research prospective cohort studies are increasingly replacing the costly controlled clinical trials, and modern machine learning methodologies like probit and tobit regressions as well as neural networks, Bayesian networks, and support vector machines prove to better fit their analysis than traditional statistical methods do.22. The current cookbook not only includes concise descriptions of standard machine learning methods, but also of more recent methods like the linear machine learning models using ordinal and loglinear regression.23. Machine learning tends to increasingly use evolutionary operation methodologies. Also this subject has been covered.24. All of the methods described have been applied in the authors' own research prior to this publication.

A unique point of this book is its low threshold, textually simple and at the same time full of self-assessment opportunities. Other unique points are the succinctness of the chapters with 3 to 6 pages, the presence of entire-commands-texts of the statistical methodologies reviewed and the fact that dull scientific texts imposing an unnecessary burden on busy and jaded professionals have been left out. For readers requesting more background, theoretical and mathematical information a note section with references is in each chapter.The first edition in 2010 was the first publication of a complete overview of SPSS methodologies for medical and health statistics. Well over 100,000 copies of various chapters were sold within the first year of publication. Reasons for a rewrite were four.First, many important comments from readers urged for a rewrite. Second, SPSS has produced many updates and upgrades, with relevant novel and improved methodologies. Third, the authorsfelt that the chapter texts needed some improvements for better readability: chapters have now been classified according the outcome data helpful for choosing your analysis rapidly, a schematic overview of data, and explanatory graphs have been added. Fourth, current data are increasingly complex and many important methods for analysis were missing in the first edition.For that latter purpose some more advanced methods seemed unavoidable, like hierarchical loglinear methods, gamma and Tweedie regressions and random intercept analyses. In order for the contents of the book to remain covered by the title, the authors renamed the book: SPSS for Starters and 2nd Levelers.Special care was, nonetheless, taken to keep things as simple as possible, simple menu commands are given. The arithmetic is still of a no-more-than high-school level. Step-by-step analyses of different statistical methodologies are given with the help of 60 SPSS data files available through the internet. Because of the lack of time of this busy group of people, the authors have given every effort to produce a text as succinct as possible.

Inmedical and health care the scientific method is little used, and statisticalsoftware programs are experienced as black box programs producing lots ofp-values, but little answers to scientific questions. The pocket calculatoranalyses appears to be, particularly, appreciated, because they enable medicaland health professionals and students for the first time to understand thescientific methods of statistical reasoning and hypothesis testing. So much so,that it can start something like a new dimension in their professional world. Inaddition, anumber of statistical methods like power calculations and required sample sizecalculations can be performed more easily on a pocket calculator, than using asoftware program. Also, there are some specific advantages of thepocket calculator method. You better understand what you are doing. The pocketcalculator works faster, because far less steps have to be taken, averages canbe used. The current nonmathematical book is complementary to thenonmathematical "SPSS for Starters and 2nd Levelers" (Springer HeidelbergGermany 2015, from the same authors), and can very well be used as its dailycompanion.

Third, the authorsfelt that the chapter texts needed some improvements for better readability: chapters have now been classified according the outcome data helpful for choosing your analysis rapidly, a schematic overview of data, and explanatory graphs have been added.

Adequate health and health care will, however, soon be impossible without proper data supervision from modern machine learning methodologies like cluster models, neural networks and other data mining methodologies.Each chapter starts with purposes and scientific questions.

This textbook consists of ten chapters, and is a must-read to all medical and health professionals, who already have basic knowledge of how to analyze their clinical data, but still, wonder, after having done so, why procedures were performed the way they were. The book is also a must-read to those who tend to submerge in the flood of novel statistical methodologies, as communicated in current clinical reports, and scientific meetings.In the past few years, the HOW-SO of current statistical tests has been made much more simple than it was in the past, thanks to the abundance of statistical software programs of an excellent quality. However, the WHY-SO may have been somewhat under-emphasized. For example, why do statistical tests constantly use unfamiliar terms, like probability distributions, hypothesis testing, randomness, normality, scientific rigor, and why are Gaussian curves so hard, and do they make non-mathematicians getting lost all the time? Thebook will cover the WHY-SOs.

Modern meta-analyses do more than combine the effect sizes of a series of similar studies. Meta-analyses are currently increasingly applied for any analysis beyond the primary analysis of studies, and for the analysis of big data. This 26-chapter book was written for nonmathematical professionals of medical and health care, in the first place, but, in addition, for anyone involved in any field involving scientific research. The authors have published over twenty innovative meta-analyses from the turn of the century till now. This edition will review the current state of the art, and will use for that purpose the methodological aspects of the authors' own publications, in addition to other relevant methodological issues from the literature.Are there alternative works in the field? Yes, there are, particularly in the field of psychology. Psychologists have invented meta-analyses in 1970, and have continuously updated methodologies. Although very interesting, their work, just like the whole discipline of psychology, is rather explorative in nature, and so is their focus to meta-analysis. Then, there is the field of epidemiologists. Many of them are from the school of angry young men, who publish shocking news all the time, and JAMA and other publishers are happy to publish it. The reality is, of course, that things are usually not as bad as they seem. Finally, some textbooks, written by professional statisticians, tend to use software programs with miserable menu programs and requiring lots of syntax to be learnt. This is prohibitive to clinical and other health professionals.   The current edition is the first textbook in the field of meta-analysis entirely written by two clinical scientists, and it consists of many data examples and step by step analyses, mostly from the authors' own clinical research.

Inmedical and health care the scientific method is little used, and statisticalsoftware programs are experienced as black box programs producing lots ofp-values, but little answers to scientific questions.

This textbook consists of ten chapters, and is a must-read to all medical and health professionals, who already have basic knowledge of how to analyze their clinical data, but still, wonder, after having done so, why procedures were performed the way they were. The book is also a must-read to those who tend to submerge in the flood of novel statistical methodologies, as communicated in current clinical reports, and scientific meetings.In the past few years, the HOW-SO of current statistical tests has been made much more simple than it was in the past, thanks to the abundance of statistical software programs of an excellent quality. However, the WHY-SO may have been somewhat under-emphasized. For example, why do statistical tests constantly use unfamiliar terms, like probability distributions, hypothesis testing, randomness, normality, scientific rigor, and why are Gaussian curves so hard, and do they make non-mathematicians getting lost all the time? Thebook will cover the WHY-SOs.

The current edition is the first textbook in the field of meta-analysis entirely written by two clinical scientists, and it consists of many data examples and step by step analyses, mostly from the authors' own clinical research.

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".