Clinical Data Analysis on a Pocket Calculator

The authorsare well-qualified in their field. Professor Zwinderman is past-president ofthe International Society of Biostatistics (2012-2015), and Professor Cleophasis past-president of the AmericanCollege of Angiology(2000-2002). From their expertise they should be able to make adequateselections of modern methods for clinical data analysis for the benefit ofphysicians, students, and investigators. The authors have been working andpublishing together for 17 years, and their research can be characterized as acontinued effort to demonstrate that clinical data analysis is not mathematicsbut rather a discipline at the interface of biology and mathematics.The authors as professors and teachers instatistics at universities in The Netherlands and France for the most part oftheir lives, are convinced that the scientific method of statistical reasoningand hypothesis testing is little used by physicians and other health workers,and they hope that the current productionwill help them find the appropriateways for answering their scientific questions.   Threetextbooks complementary to the current production and written by the sameauthors are Statistics applied to clinical studies 5th edition, 2012, Machinelearning in medicine a complete overview, 2015, SPSS for starters and 2ndlevelers, 2015, all of them edited by Springer Heidelberg Germany.

• Preface.-I Continuous Outcome Data.- Data Spread, Standard Deviations.- Data Summaries:Histograms, Wide and Narrow Gaussian Curves.- Null-Hypothesis Testing withGraphs.- Null-Hypothesis Testing with the T-table.- One-Sample Continuous Data (One-SampleT-Test, One-Sample Wilcoxon.- Paired Continuous Data (Paired T-Test, Two-SampleWilcoxon Signed Rank Test).- Unpaired Continuous Data (Unpaired T-Test,Mann-Whitney).- Linear Regression (Regression Coefficients, CorrelationCoefficients, and their   StandardErrors).- Kendall-Tau Regressionfor Ordinal Data.- Paired Continuous Data, Analysis with Help of CorrelationCoefficients.- Power Equations.- Sample Size Calculations.- ConfidenceIntervals.- Equivalence Testing instead of Null-Hypothesis Testing.- NoninferiorityTesting instead of Null-Hypothesis Testing.- Superiority Testing instead ofNull-Hypothesis Testing.- Missing Data Imputation.- Bonferroni  Adjustments.- Unpaired Analysis of Variance(ANOVA).- Paired Analysis of Variance (ANOVA).-VariabilityAnalysis for One or Two Samples.- 22 Variability Analysis for Three or More Samples.-Confounding.- Propensity Score and Propensity Score Matching for MultipleConfounders.- Interaction.- Accuracy and Reliability Assessments.- Robust Testsfor Imperfect Data.- Non-linear Modeling on a Pocket Calculator.- FuzzyModeling for Imprecise and Incomplete Data.-Bhattacharya Modeling for Unmasking Hidden Gaussian Curves.- Item ResponseModeling instead of Classical Linear Analysis of Questionnaires.- Meta-Analysis.- Goodness of Fit Tests for Identifying Nonnormal Data.-Non-Parametric Tests for Three or More Samples (Friedman and Kruskal-Wallis).- IIBinary Outcome Data.-Data Spread: StandardDeviation, One Sample Z- Test, One Sample Binomial Test.- Z-Tests.- Phi Testsfor Nominal Data.- 38 Chi-Square Tests.-Fisher Exact Tests Convenient for Small Samples.- Confounding.- Interaction.- Chi-squareTests for Large Cross-Tabs.- Logarithmic Transformations, a Great Help to StatisticalAnalyses .- Odds Ratios, a Short-Cut for AnalyzingCross-Tabs.- Log odds, the Basis of Logistic Regression.- Log Likelihood Ratio Testsfor the Best Precision.- Hierarchical Loglinear Models for Higher Order Cross-Tabs.- McNemar Tests for Paired Cross-Tabs.-McNemar Odds Ratios.- Power Equations.- Sample Size Calculations.- AccuracyAssessments.- Reliability Assessments.- Unmasking Fudged Data.- Markov Modelingfor Predictions outside the Range of Observations.-  Binary Partitioning with CART (Classificationand Regression Tree) Methods.- Meta-Analysis.- Physicians' Daily Life and theScientific Method.- Incident Analysis and the Scientific Method.- Cochran Testsfor Large Paired Cross-Tabs.-Index.