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Frontmatter
1. Continuous Outcome Data
1. One-Sample Continuous Data (One-Sample T-Test, One-Sample Wilcoxon Signed Rank Test, 10 Patients)
2. Paired Continuous Data (Paired T-Test, Wilcoxon Signed Rank Test, 10 Patients)
3. Paired Continuous Data with Predictors (Generalized Linear Models, 50 Patients)
4. Unpaired Continuous Data (Unpaired T-Test, Mann-Whitney, 20 Patients)
5. Linear Regression (20 Patients)
6. Multiple Linear Regression (20 Patients)
7. Automatic Linear Regression (35 Patients)
8. Linear Regression with Categorical Predictors (60 Patients)
9. Repeated Measures Analysis of Variance, Friedman (10 Patients)
10. Repeated Measures Analysis of Variance Plus Predictors (10 Patients)
11. Doubly Repeated Measures Analysis of Variance (16 Patients)
12. Repeated Measures Mixed-Modeling (20 Patients)
13. Unpaired Continuous Data with Three or More Groups (One Way Analysis of Variance, Kruskal-Wallis, 30 Patients)
14. Automatic Nonparametric Testing (30 Patients)
15. Trend Test for Continuous Data (30 Patients)
16. Multistage Regression (35 Patients)
17. Multivariate Analysis with Path Statistics (35 Patients)
18. Multivariate Analysis of Variance (35 and 30 Patients)
19. Missing Data Imputation (35 Patients)
20. Meta-regression (20 and 9 Studies)
21. Poisson Regression for Outcome Rates (50 Patients)
22. Confounding (40 Patients)
23. Interaction, Random Effect Analysis of Variance (40 Patients)
24. General Loglinear Models for Identifying Subgroups with Large Health Risks (12 Populations)
25. Curvilinear Estimation (20 Patients)
26. Loess and Spline Modeling (90 Patients)
27. Monte Carlo Tests for Continuous Data (10 and 20 Patients)
28. Artificial Intelligence Using Distribution Free Data (90 Patients)
29. Robust Testing (33 Patients)
30. Nonnegative Outcomes Assessed with Gamma Distribution (110 Patients)
31. Nonnegative Outcomes Assessed with Tweedie Distribution (110 Patients)
32. Validating Quantitative Diagnostic Tests (17 Patients)
33. Reliability Assessment of Quantitative Diagnostic Tests (17 Patients)
2. Binary Outcome Data
34. One-Sample Binary Data (One-Sample Z-Test, Binomial Test, 55 Patients)
35. Unpaired Binary Data (Chi-Square Test, 55 Patients)
36. Logistic Regression with a Binary Predictor (55 Patients)
37. Logistic Regression with a Continuous Predictor (55 Patients)
38. Logistic Regression with Multiple Predictors (55 Patients)
39. Logistic Regression with Categorical Predictors (60 Patients)
40. Trend Tests for Binary Data (106 Patients)
41. Paired Binary (McNemar Test) (139 General Practitioners)
42. Paired Binary Data with Predictor (139 General Practitioners)
43. Repeated Measures Binary Data (Cochran’s Q Test), (139 Patients)
44. Multinomial Regression for Outcome Categories (55 Patients)
45. Random Intercept for Categorical Outcome and Predictor Variables (55 Patients)
46. Comparing the Performance of Diagnostic Tests (650 and 588 Patients)
47. Poisson Regression for Binary Outcomes (52 Patients)
48. Ordinal Regression for Data with Underpresented Outcome Categories (450 Patients)
49. Probit Regression, Binary Data as Response Rates (14 Tests)
50. Monte Carlo Tests for Binary Data (139 Physicians and 55 Patients)
51. Loglinear Models, Logit Loglinear Models (445 Patients)
52. Loglinear Models, Hierarchical Loglinear Models (445 Patients)
53. Validating Qualitative Diagnostic Tests (575 Patients)
54. Reliability Assessment of Qualitative Diagnostic Tests (17 Patients)
3. Survival and Longitudinal Data
55. Log Rank Testing (60 Patients)
56. Cox Regression With/Without Time Dependent Variables (60 Patients)
57. Segmented Cox Regression (60 Patients)
58. Assessing Seasonality (24 Averages)
59. Interval Censored Data Analysis for Assessing Mean Time to Cancer Relapse (51 Patients)
60. Polynomial Analysis of Circadian Rhythms (1 Patient with Hypertension)
Backmatter
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