Kernel Ridge Regression in Clinical Research

Description

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 the kernel 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. Preface Contents Chapter 1: Traditional Kernel Regression 1.1 Summary 1.2 Introduction 1.3 Kernel Regression 1.4 Conclusion 1.5 References Chapter 2: Kernel Ridge Regression (KRR) 2.1 Summary 2.2 History of Kernel Ridge Regression 2.3 Kernel Density Modeling 2.4 The Kernel Trick 2.5 Ridge Regularization 2.6 Conclusion 2.7 References Chapter 3: Optimal Scaling vs Kernel Ridge Regression 3.1 Summary 3.1.1 Summaries of the Traditional Linear Regressions 3.1.2 Summaries of the Kernel Ridge Regressions 3.1.3 In Conclusion 3.2 Introduction 3.3 Optimal Scaling 3.4 Traditional Regressions 3.5 Kernel Ridge Regressions Scale 1 3.6 Kernel Ridge Regressions Scale 2 3.7 Kernel Ridge Regressions Scale 3 3.8 Conclusion 3.8.1 Summary of the Traditional R Square Values of the Scales 1-3 Models 3.8.2 Summary of Kernel Ridge Regressions (KRR) 3.9 References Chapter 4: Examples of Published Kernel Ridge Regressions So Far 4.1 Summary 4.2 Introduction 4.3 History of Kernel Ridge Regression 4.4 A Brief Search of Kernel Ridge Regression Publications So Far 4.5 Courses Where the Upcoming Edition ``Kernel Ridge Regression in Clinical Research´´ Will Be Used 4.6 Conclusion 4.7 References Chapter 5: Some Terminology 5.1 Summary 5.2 Alphabetical Enumeration 5.3 References Chapter 6: Effect on Being Blind of Age/Sex Adjusted Mortality of Onchocerciasis Patients in 12,816 Personyears, Traditional v... 6.1 Summary 6.1.1 Summaries of Traditional Regressions 6.1.2 Summaries of Kernel Ridge Regressions 6.2 Introduction 6.3 Data Example 6.4 Traditional Linear Regression 6.5 Kernel Ridge Regressions 6.6 Conclusion 6.6.1 Summaries of Traditional Regressions 6.6.2 Summaries of Kernel Ridge Regressions 6.7 References Chapter 7: Effect of Old Treatment on New Treatment, 35 Patients, Traditional Regressions vs Kernel Ridge Regressions 7.1 Summary 7.1.1 Summaries of Traditional Regressions 7.1.2 Summaries of Kernel Ridge Regressions 7.2 Introduction 7.3 Data Example 7.4 Traditional Linear Regression 7.5 Robust Regression 7.6 Quantile Regressions 7.7 Kernel Ridge Regression 7.8 Conclusion 7.8.1 Summaries of Traditional Regressions 7.8.2 Summaries of Kernel Ridge Regressions 7.9 References Chapter 8: Effect of Gene Expressions on Drug Efficacy, 250 Patients, Traditional Regressions vs Kernel Ridge Regression 8.1 Summary 8.1.1 Summaries of Traditional Regressions 8.1.2 Summaries of Kernel Ridge Regressions 8.2 Introduction 8.3 Data Example 8.4 Traditional Linear Regression 8.5 Kernel Ridge Regression 8.6 Conclusion 8.6.1 Summaries of Traditional Regressions 8.6.2 Summaries of Kernel Ridge Regressions 8.7 References Chapter 9: Effect of Gender, Treatment, and Their Interactions on Numbers of Paroxysmal Atrial Fibrillations, 40 Patients, Tra... 9.1 Summary 9.1.1 Summaries of Two Predictor Regressions 9.1.2 Summaries of Three Predictor Regressions 9.2 Introduction 9.3 Data Example 9.4 Traditional Linear Regression with Two Predictors 9.5 Kernel Ridge Regression with Two Predictors 9.6 Traditional Linear Regression with Three Predictors 9.7 Kernel Ridge Regression with Three Predictors 9.8 Conclusion 9.8.1 Summaries of Two Predictor Regressions 9.8.2 Summaries of Three Predictor Regressions 9.9 References Chapter 10: Effect of Laboratory Predictors on Septic Mortality, 200 Patients, Traditional Regression vs Kernel Ridge Regressi... 10.1 Summary 10.1.1 Summaries of the Traditional Regressions 10.1.2 Summaries of the Kernel Ridge Regressions 10.2 Introduction 10.3 Data Example 10.4 Test and Retest Reliability 10.5 Binary Logistic Regression 10.6 Kernel Ridge Regression 10.7 Conclusion 10.7.1 Summaries of the Traditional Regressions 10.7.2 Summaries of the Kernel Ridge Regressions 10.8 References Chapter 11: Effect of Month on Mean C-Reactive Protein, 18 Months, Traditional Regressions vs Kernel Ridge Regression 11.1 Summary 11.1.1 Summaries of Traditional Regressions 11.1.1.1 Autocorrelations 11.1.1.2 Curvilinear Regressions 11.1.2 Summaries of Kernel Ridge Regressions 11.2 Introduction 11.2.1 Summaries of Traditional Regressions 11.2.2 Summaries of Kernel Ridge Regressions 11.3 Autoregression Analysis 11.4 Data Example 11.5 Assessing Seasonality with Autocorrelations 11.6 Assessing Seasonality with Curvilinear Regressions 11.7 Assessing Seasonality with Kernel Ridge Regressions 11.8 Conclusions 11.8.1 Summaries of Traditional Regressions 11.8.2 Summaries of Kernel Ridge Regressions 11.9 References Chapter 12: Effect of Different Dosages of Prednisone and Beta-Agonist on Peakflow, 78 Patients, Traditional Regressions vs Ke... 12.1 Summary 12.1.1 Summaries of Traditional Regressions 12.1.2 Summaries of Kernel Ridge Regressions 12.2 Introduction 12.3 Data Example (Var = Variable) 12.4 Regressions with Inconstant Variability 12.5 Traditional Linear Regression 12.6 Weighted Least Squares Regression 12.7 Kernel Ridge Regression 12.8 Conclusion 12.8.1 Summaries of Traditional Regressions 12.8.2 Summaries of Kernel Ridge Regressions 12.9 References Chapter 13: Effect of Race, Age, and Gender on Physical Strength, 60 Patients, Traditional Regressions vs Kernel Ridge Regress... 13.1 Summary 13.1.1 Summaries of Traditional Regressions 13.1.2 Summaries of Kernel Ridge Regressions 13.2 Introduction 13.3 Data Example 13.4 Restructuring Traditional Multiple Variables Regression 13.5 Unrestructured Traditional Regression 13.6 Restructured Traditional Regression 13.7 Unrestructured Kernel Ridge Regressions with Race as Categorical Predictor Variable 13.8 Restructured Kernel Ridge Regressions 13.9 Conclusion 13.9.1 Summaries of Traditional Regressions 13.9.2 Summaries of Kernel Ridge Regressions 13.10 References Chapter 14: Effect of Treatment, Age, Gender, and Co-morbidity on Hours of Sleep, 20 Patients, Traditional Regression vs Kerne... 14.1 Summary 14.1.1 Summaries of Traditional Linear Regressions 14.1.2 Summaries of Kernel Ridge Regressions 14.2 Introduction 14.3 Data Example 14.4 Traditional Regression 14.5 Kernel Ridge Regression 14.6 Conclusion 14.6.1 Summaries of Traditional Linear Regressions 14.6.2 Summaries of Kernel Ridge Regressions 14.7 References Chapter 15: Effect of Counseling and Non-compliance on Monthly Stools, 35 Constipated Patients, Traditional Regressions vs Ker... 15.1 Summary 15.1.1 Summaries of Traditional Regressions 15.1.2 Summaries of Kernel Ridge Regressions 15.2 Introduction 15.3 Data Example 15.4 Traditional Regression Analysis 15.5 Two Stage Least Squares (2SLS) 15.6 Kernel Ridge Regression 15.7 Conclusion 15.7.1 Summaries of Traditional Regressions 15.7.2 Summaries of Kernel Ridge Regressions 15.8 References Chapter 16: Effect of Treatment Modality, Counseling, and Satisfaction with Doctor on Quality of Life, 450 Patients, Tradition... 16.1 Summary 16.1.1 Summaries of Traditional Regressions 16.1.2 Summaries of Kernel Density Regressions 16.2 Introduction 16.3 Data Example 16.4 Traditional Linear Regression 16.5 Multinomial Regression 16.6 Ordinal Regression 16.7 Kernel Ridge Regression 16.8 Conclusions 16.8.1 Summaries of Traditional Regressions 16.8.2 Summaries of Kernel Density Regressions 16.9 References Chapter 17: Effect of Department and Patient-Age on Risk of Falling out of Bed, 55 Patients, Traditional Regression vs Kernel ... 17.1 Summary 17.1.1 Summary of Traditional Regression with Multinomial Logistic Regression 17.1.2 Summary of Kernel Ridge Regression 17.2 Introduction 17.3 Data Example 17.4 Traditional Regression with Multinomial Logistic Regression 17.5 Kernel Ridge Regression 17.6 Conclusions 17.6.1 Traditional Regression with Multinomial Logistic Regression 17.6.2 Kernel Ridge Regressions 17.7 References Chapter 18: Effect of Diet, Gender, Sport, and Medical Treatment on LDL Cholesterol Reduction, 953 Patients, Traditional Regre... 18.1 Summary 18.1.1 Summaries of the Traditional Multiple Variables Linear Regressions 18.1.2 Summaries of Kernel Ridge Regressions 18.2 Introduction 18.3 Data Example 18.4 Traditional Linear Regression 18.4.1 Decision Tree Analysis (Exhaustive Testing) 18.5 Kernel Ridge Regression 18.6 Conclusions 18.6.1 Summaries of the Traditional Multiple Variables Linear Regressions 18.6.2 Summaries of Kernel Ridge Regressions 18.7 References Chapter 19: Effect of Gender, Age, Weight, and Height on Measured Body Surface, 90 Patients, Traditional Regression vs Kernel ... 19.1 Summary 19.1.1 Summaries of the Traditional Multiple Variables Linear Regression 19.1.2 Summaries of the Kernel Ridge Regression 19.2 Introduction 19.3 Data Example 19.4 Traditional Linear Regression 19.5 Kernel Ridge Regression 19.6 Conclusions 19.6.1 Summaries of the Traditional Multiple Variables Linear Regression 19.6.2 Summaries of the Kernel Ridge Regression 19.7 References Chapter 20: Effect of Physicians´ Characteristics on Their Inclination to Give Lifestyle Advise or Not, 139 Physicians, Tradit... 20.1 Summary 20.1.1 Summaries of Traditional Multiple Variables Logistic Regressions 20.1.2 Summaries of Kernel Ridge Regressions 20.2 Introduction 20.3 Patient Example 20.4 Traditional Regression (Binary Logistic Regression) 20.5 Kernel Ridge Regression 20.6 Conclusion 20.6.1 Summaries of Traditional Multiple Variables Logistic Regressions 20.6.2 Summaries of Kernel Ridge Regressions 20.7 References Chapter 21: Effect of Treatment, Psychological and Social Scores on Numbers of Paroxysmal Atrial Fibrillations, 50 Patients, T... 21.1 Summary 21.1.1 Summary of Traditional Regressions 21.1.2 Summary of Kernel Ridge Regressions 21.2 Introduction 21.3 Data Example 21.4 Traditional Linear Regressions 21.5 Kernel Ridge Regression 21.6 Conclusion 21.6.1 Summary of Traditional Regressions 21.6.2 Summary of Kernel Ridge Regressions 21.7 References Chapter 22: Effect of Various Predictors on Numbers of Convulsions in 3390 Patients, Traditional vs Kernel Ridge Regression 22.1 Summary 22.1.1 Summaries of Traditional Multiple Variables Linear Regressions 22.1.2 Summaries of Kernel Ridge Linear Regressions 22.2 Introduction 22.3 Data Example 22.4 Traditional Multiple Variables Regressions 22.5 Kernel Ridge Regression 22.6 Conclusion 22.6.1 Summaries of Traditional Multiple Variables Linear Regressions 22.6.2 Summaries of Kernel Ridge Linear Regressions 22.7 References Chapter 23: Effect of Foods Served on Breakfast Taken, 252 Persons, Traditional Linear and Multinomial Logistic Regression vs ... 23.1 Summary 23.1.1 Summaries of the Traditional Multiple Variables Linear Regression and Multinomial Logistic Regression 23.1.2 Summaries of the Kernel Ridge Regressions 23.2 Introduction 23.3 Data Example 23.4 Traditional Linear Regression and Multinomial Logistic Regression 23.5 Kernel Ridge Regressions 23.6 Conclusion 23.6.1 Summaries of the Traditional Multiple Variables Linear Regression 23.6.2 Summaries of the Kernel Ridge Regressions 23.7 References Chapter 24: Effect of Personal Factors on Anorexia, 217 Persons, Traditional Linear Regression vs Kernel Ridge Regressions 24.1 Summary 24.1.1 Summaries of the Traditional Multiple Variables Linear Regression 24.1.2 Summaries of the Kernel Ridge Regression 24.2 Introduction 24.3 Data Example 24.4 Traditional Linear Regression 24.5 Kernel Ridge Regressions 24.6 Conclusions 24.6.1 Summaries of the Traditional Multiple Variables Linear Regression 24.6.2 Summaries of the Kernel Ridge Regression 24.7 References Chapter 25: Effect on Weight Loss of Physical Exercise, Calorie Intake, Their Interaction, and Age, in 64 Patients, Traditiona... 25.1 Summary 25.1.1 Summaries of the Traditional Multiple Variables Linear Regression 25.1.2 Summaries of Kernel Ridge Regressions 25.2 Introduction 25.3 Data Example 25.4 Traditional Linear Regression 25.5 Kernel Ridge Regression 25.6 Conclusion 25.6.1 Summaries of the Traditional Multiple Variables Linear Regression 25.6.2 Summaries of Kernel Ridge Regressions 25.7 References Chapter 26: Summaries 26.1 Chapter 1 26.2 Chapter 2 26.3 Chapter 3 26.3.1 Summaries of the Traditional Linear Regressions 26.3.2 Summaries of the Kernel Ridge Regressions 26.3.3 In Conclusion 26.4 Chapter 4 26.5 Chapter 5 26.6 Chapter 6 26.6.1 Summaries of Traditional Regressions 26.6.2 Summaries of Kernel Ridge Regressions 26.7 Chapter 7 26.7.1 Summaries of Traditional Regressions 26.7.2 Summaries of Kernel Ridge Regressions 26.8 Chapter 8 26.8.1 Summaries of Traditional Regressions 26.8.2 Summaries of Kernel Ridge Regressions 26.9 Chapter 9 26.9.1 Summaries of Two Predictor Regressions 26.9.2 Summaries of Three Predictor Regressions 26.10 Chapter 10 26.10.1 Summaries of the Traditional Regressions 26.10.2 Summaries of the Kernel Ridge Regressions 26.11 Chapter 11 26.11.1 Summaries of Traditional Regressions 26.11.1.1 Autocorrelations 26.11.1.2 Curvilinear Regressions 26.11.2 Summaries of Kernel Ridge Regressions 26.12 Chapter 12 26.12.1 Summaries of Traditional Regressions 26.12.2 Summaries of Kernel Ridge Regressions 26.13 Chapter 13 26.13.1 Summaries of Traditional Regressions 26.13.2 Summaries of Kernel Ridge Regressions 26.14 Chapter 14 26.14.1 Summaries of Traditional Linear Regressions 26.14.2 Summaries of Kernel Ridge Regressions 26.15 Chapter 15 26.15.1 Summaries of Traditional Linear Regressions 26.15.2 Summaries of Kernel Ridge Regressions 26.16 Chapter 16 26.16.1 Summaries of Traditional Regressions 26.16.2 Summaries of Kernel Density Regressions 26.17 Chapter 17 26.17.1 Summary of Traditional Regression with Multinomial Logistic Regression 26.17.2 Summary of Kernel Ridge Regression 26.18 Chapter 18 26.18.1 Summaries of the Traditional Multiple Variables Linear Regressions 26.18.2 Summaries of Kernel Ridge Regressions 26.19 Chapter 19 26.19.1 Summaries of the Traditional Multiple Variables Linear Regression 26.19.2 Summaries of the Kernel Ridge Regression 26.20 Chapter 20 26.20.1 Summaries of Traditional Multiple Variables Logistic Regressions 26.20.2 Summaries of Kernel Ridge Regressions 26.21 Chapter 21 26.21.1 Summary of Traditional Regressions 26.21.2 Summary of Kernel Ridge Regressions 26.22 Chapter 22 26.22.1 Summaries of Traditional Multiple Variables Linear Regressions 26.22.2 Summaries of Kernel Ridge Linear Regressions 26.23 Chapter 23 26.23.1 Summaries of the Traditional Multiple Variables Linear Regression and Multinomial Logistic Regression 26.23.2 Summaries of the Kernel Ridge Regressions 26.24 Chapter 24 26.24.1 Summaries of the Traditional Multiple Variables Linear Regression 26.24.2 Summaries of the Kernel Ridge Regression 26.25 Chapter 25 26.25.1 Summaries of the Traditional Multiple Variables Linear Regression 26.25.2 Summaries of Kernel Ridge Regressions