Preface
I Continuous Outcome Data
1 Data Spread, Standard Deviations
2 Data Summaries: Histograms, Wide and Narrow Gaussian Curves
3 Null-Hypothesis Testing with Graphs
4 Null-Hypothesis Testing with the T-table
5 One-Sample Continuous Data (One-Sample T-Test, One-Sample Wilcoxon
6 Paired Continuous Data (Paired T-Test, Two-Sample Wilcoxon Signed Rank Test)
7 Unpaired Continuous Data (Unpaired T-Test, Mann-Whitney) 8 Linear Regression (Regression Coefficients, Correlation Coefficients, and their Standard Errors)
9 Kendall-Tau Regression for Ordinal Data
10 Paired Continuous Data, Analysis with Help of Correlation Coefficients
11 Power Equations
12 Sample Size Calculations
13 Confidence Intervals 14 Equivalence Testing instead of Null-Hypothesis Testing
15 Noninferiority Testing instead of Null-Hypothesis Testing
16 Superiority Testing instead of Null-Hypothesis Testing
17 Missing Data Imputation
18 Bonferroni Adjustments
19 Unpaired Analysis of Variance (ANOVA) 20 Paired Analysis of Variance (ANOVA)
21 Variability Analysis for One or Two Samples
22 Variability Analysis for Three or More Samples
23 Confounding
24 Propensity Score and Propensity Score Matching for Multiple Confounders
25 Interaction
26 Accuracy and Reliability Assessments 27 Robust Tests for Imperfect Data
28 Non-linear Modeling on a Pocket Calculator
29 Fuzzy Modeling for Imprecise and Incomplete Data
30 Bhattacharya Modeling for Unmasking Hidden Gaussian Curves
31 Item Response Modeling instead of Classical Linear Analysis of Questionnaires
32 Meta-Analysis 1 33 Goodness of Fit Tests for Identifying Nonnormal Data
34 Non-Parametric Tests for Three or More Samples (F
About the Author:
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 methods for clinical data analysis for the benefit of physicians, students, and investigators. The authors have been working and publishing together for 17 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 convinced that the scientific method of statistical reasoning and hypothesis testing is little used by physicians and other health workers, and they hope that the current production will help them find the appropriate ways for answering their scientific questions.
Three 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, 2015, all of them edited by Springer Heidelberg Germany.
