Statistical Methods and Reasoning for the Clinical Sciences: Evidence-Based Practice.pdf

Statistical Methods and Reasoning for the Clinical Sciences: Evidence-Based Practice.pdf


Eiki Satake, Ph.D., Associate Professor of Mathematics/Statistics, Department of Communication Sciences and Disorders, Emerson College.

Introduction: Philosophical Preliminaries for Clinical Statistics 0.1. Overview: Why Is Statistics Important? 0.2. What Is Statistical Reasoning? 0.3. How to Become a Better Clinician 0.4. What is 'Evidence-Based' Practice? 0.5. Future Role of a Clinician 0.6. Moving Forward to Evidence Based Statistics: What really prevents us? 0.7. Scientific Literacy and Ethical Practice: Time for a Check-Up? 0.8. Exploring an Alternative Statistical Method Chapter 1: What is Statistics? 1.1. Population and Sample 1.2. Fundamentals of Statistical Inference 1.3. Statistical Notations 1.4. Key Terms and Concepts 1.5. Step-by-Step Guides for Review 1.6 Clinical Case Studies 1.7 Exercises Chapter 2: Collecting and Organizing Data 2.1. Frequency Distribution 2.2. Class Interval Distribution 2.3. Graphs and Charts 2.4. Key Terms and Concepts 2.5. Step-by-Step Guides for Review 2.6. Clinical Case Studies 2.7. Exercises Chapter 3: Descriptive Methods 3.1. Measures of Central Tendency 3.2. Measures of Dispersion 3.3. Key Terms and Concepts 3.4. Step-by-Step Guides for Review 3.5. Clinical Case Studies 3.6. Exercises Chapter 4: Fundamentals of Correlation and Regression 4.1. Pearson Product-Moment Correlation Coefficient R 4.2. Spearman Rank Corellation Coefficient Rho 4.3. Simple Linear Regression 4.4. Multiple Linear Regression: Overview 4.5. Logistic Regression: Overview 4.6. Key Terms and Concepts 4.7. Step-by-Step Guides for Review 4.8. Clinical Case Studies 4.9. Exercises Chapter 5: Probability: The Basis for Clinical Decision-Making 5.1. The Importance of Probability in the Clinical Sciences 5.2. Probability Theory and Statistical Inference 5.3. Probability and Level of Confidence 5.4. Logical and Mathematical Basis of Probability 5.5. Different Kinds of Events 5.6. Types and Rules of Probability: Discrete and Binomial 5.7. Conditional Probability and the Bayes' Rule 5.8. Deductive Inference and Inductive Inference 5.9. The Bayesian View of Probability 5.10. Determining the Accuracy of Diagnostic Testing 5.11. Key Terms and Concepts 5.12. Step-by-Step Guides for Review 5.13. Clinical Case Studies 5.14. Exercises Chapter 6: Probability Distribution for Continuous Random Variables 6.1. Continuous Probability Distributions 6.2. The Normal Distribution 6.3. The Standard Normal Distribution 6.4. Calculation of Probability in Normal Distribution 6.5. Normal Approximation to the Binomial Distribution 6.6. The Central Limit Theorem 6.7. Key Terms and Concepts 6.8. Step-by-Step Guides for Review 6.9. Clinical Case Studies 6.10. Exercises Chapter 7: Statistical Inference Concerning One Parameter 7.1. Hypothesis Testing Concerning a Population Menu Case 1: Large Sample Case 2: Small Sample 7.2. Classical Approach and P-value Approach 7.3. Hypothesis Concerning a Population Proportion 7.4. Parametric Assumptions 7.5. Type-I Error, Type-II Error and Power of a Test 7.6. Confidence Interval Concerning a Population Menu 7.7. Confidence Interval Concerning a Population Proportion 7.8. Power and Sample Size Determination 7.9. Key Terms and Concepts 7.10. Step-by-Step Guides for Review 7.11. Clinical Case Studies 7.12. Exercises Chapter 8: Statistical Inference Concerning Two Parameters 8.1. Paired Sample 8.2. Unpaired Sample: Large-Sample Case 8.3. F Distribution: Homogeneity of Variances 8.4. Unpaired Sample: Small-Sample 8.5. Inference Concerning Two Proportions 8.6. Confidence Interval Concerning Two Parameters 8.7. Power and Sample Size Determination 8.8. Key Terms and Concepts 8.9. Step-by-Step Guides for Review 8.10. Clinical Case Studies 8.11. Exercises Chapter 9: Inference Concerning Correlation and Regression 9.1. Inference Concerning Correlation 9.2. The Linear Regression Model: Overview 9.3. Inference Concerning Slope 9.4. Prediction Intervals and Confidence Intervals 9.5. Key Terms and Concepts 9.6. Step-by-Step Guides for Review 9.7. Clinical Case Studies 9.8. Exercises Chapter 10: Analysis of Variance (ANOVA) 10.1. Logic Behind ANOVA 10.2. One-Way ANOVA 10.3. Multivariate ANOVA (MANOVA) 10.4. Two-Way ANOVA: Non-interaction Model 10.5. Two-Way ANOVA: Interaction Model 10.6. Three-Way ANOVA: Latin Square Design 10.7. Randomized-Blocks ANOVA (RBANOVA) 10.8. Post-Hoc ANOVA Tests 10.9. Key Terms and Concepts 10.10. Step-by-Step Guides for Review 10.11. Clinical Case Studies 10.12. Exercises Chapter 11: Analysis of Covariance (ANCOVA) 11.1. Logic Behind ANCOVA 11.2 One-Way ANCOVA 11.3. Multivariate ANCOVA (MANCOVA) 11.4. Two-Way ANCOVA: Non-interaction Model 11.5. Two-Way ANCOVA: Interaction Model 11.6. Post-Hoc ANCOVA Tests 11.7. Key Terms and Concepts 11.8. Step-by-Step Guides for Review 11.9. Clinical Case Studies 11.10. Exercises Chapter 12: Other Statistical Methods: Logic Behind the Methods 12.1. Meta-Analysis: Effect Size 12.2. Power Analysis 12.3. Kappa Formula: The Calculation of Agreement 12.4. Bayesian Statistical Analysis: P-value Fallacy 12.5. Key Terms and Concepts 12.6. Step by Step Guides for Review 12.7. Clinical Case Studies 12.8. Exercises Chapter 13: Categorical Analysis 13.1. The Accuracy of a Diagnostic Test 13.2. Sensitivity and Specificity 13.3. Predictive Value Positive and Predictive Value Negative 13.4. Likelihood Ratio: Bayes' Factor 13.5. Chi-Square Test: Goodness-of-Fit Test 13.6. Chi-Square Test of Independence 13.7. Chi-Square Test of Homogeneity 13.8. Power and Sample Size Determination 13.9. Key Terms and Concepts 13.10. Step-by-Step Guides for Review 13.11. Clinical Case Studies 13.12. Exercises Chapter 14: Nonparametric Tests 14.1. The Wilcoxon Signed-Rank Test 14.2. Mann-Whitney U Test 14.3. The Kruskal-Wallis Test (KWANOVA) 14.4. Other Nonparametric Tests 14.5. Key Terms and Concepts 14.6. Step-by-Step Guides for Review 14.7. Clinical Case Studies 14.8. Exercises Chapter 15: Introduction to Single Subject Design (SSD) 15.1. What is SSD? 15.2. Several Types of SSD: Strengths and Limitations 15.3. Statistical Analysis for SSD 15.4. Key Terms and Concepts 15.5. Step-by-Step Guides for Review 15.6. Clinical Case Studies 15.7. Exercises Appendix I: Introduction to Statistical Computing Using SPSS/ Minitab Appendix II: Statistical Tables

The growing emphasis on evidence-based practice has increased the importance of using clinical studies for empirical demonstration of the efficacy of clinical interventions. As a result, speech-language pathologists and audiologists must be well-versed in research methods and statistical analysis. In fact, a demonstrated knowledge of statistics (including a stand-alone course in statistics) is a requirement of ASHA certification effective September 1, 2014. Statistical Methods and Reasoning for the Clinical Sciences is the ideal textbook to meet the need for a solid understanding of statistics for communication sciences and disorders. The author clearly defines and illustrates the foundational concepts of statistics, including statistical vocabulary, population parameters, sampling methods, and descriptive methods like measures, correlation, and regression. Emphasis is placed on the topic of probability because a firm grasp of the probabilistic approach is essential for any clinician to generate a precise diagnosis. The readers of this textbook will: Comprehend how clinical research reflects a series of steps that conform with the scientific method of problem solving (observation, hypothesis formation, hypothesis testing, verification, and evaluation). Appreciate the importance of including rationales in a research study that entail three interrelated tasks: description (why it was done), explanation (what was done and to whom), contextualization (how the results relate to other bodies of knowledge). Distinguish between "statistical significance" and "clinical significance." Value the importance of scientific literacy as a major ingredient of evidence practice. With its comprehensive scope and timely content Statistical Methods and Reasoning for the Clinical Sciences is the ideal text for students of communication sciences and disorders who wish to engage in truly evidence-based practice.


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