Clinical Data Analysis on a Pocket Calculator

Understanding the Scientific Methods of Statistical Reasoning and Hypothesis Testing
2. Auflage 2016
ISBN: 978-3-319-27103-3
Verlag: Springer

Buch, Englisch, 334 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 6623 g

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

Zielgruppe

Graduate

Autoren/Hrsg.

Cleophas, Ton J.

Zwinderman, Aeilko H.

Fachgebiete

• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizin, Gesundheitswesen Medizinische Mathematik & Informatik
• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Pharmazie
• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizin, Gesundheitswesen Biomedizin, Medizinische Forschung, Klinische Studien

Inhaltsverzeichnis

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 (Friedman and Kruskal-Wallis)         

II Binary Outcome Data

35 Data Spread: Standard Deviation, One Sample Z- Test, One Sample Binomial  

    Test   

36 Z-Tests            

37 Phi Tests for Nominal Data        

38 Chi-Square Tests           

39 Fisher Exact Tests Convenient for Small Samples 

40 Confounding 

41 Interaction     

42 Chi-square Tests for Large Cross-Tabs    

43 Logarithmic Transformations, a Great Help to Statistical Analyses  

44 Odds Ratios, a Short-Cut for Analyzing Cross-Tabs             

45 Logodds, the Basis of Logistic Regression               

46 Log Likelihood Ratio Tests for the Best Precision  

47 Hierarchical Loglinear Models for Higher Order Cross-Tabs               

48 McNemar Tests for Paired Cross-Tabs     

49 McNemar Odds Ratios

50 Power Equations           

51 Sample Size Calculations                            

52 Accuracy Assessments

53 Reliability Assessments               

54 Unmasking Fudged Data            

55 Markov Modeling for Predictions outside the Range of Observations               

56 Binary Partitioning with CART (Classification and Regression Tree) Methods               

57 Meta-Analysis               

58 Physicians' Daily Life and the Scientific Method   

59 Incident Analysis and the Scientific Method          

60 Cochran Tests for Large Paired Cross-Tabs            

Index

Über Autor(innen)

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.