Textbook in PDF format

Every practicing physician, surgeon, advanced practice provider, and allied health professional interacts regularly with peer-reviewed literature: either while creating it, or consuming it. Despite the countless hours over many years spent in formal clinical training, many clinicians and clinician-authors lack advanced training or a working nuanced knowledge of research methodology and study design. Institutions have responded to this gap by reinforcing their ranks with statistical and methodological support in the form of data analysts, epidemiologists, and biostatisticians. However, clinicians are often unable to “talk the methodological talk” to guide them. This ultimately results in a stark disconnect between clinically relevant aspects of research and appropriate study design.
Existing research methodology texts are largely written by statisticians, epidemiologists, and other academic public health experts. These are not easily digestible by practicing clinicians who need practical knowledge of this content to design their own research or enhance their understanding of the medical literature. Furthermore, these texts are often too detailed or “in the weeds” with regard to mathematics and statistical mechanics. Practical knowledge is not centrally located; rather, it is spread out among multiple books, articles, and other sources.
This book is a concise, accessible, and practical guide for clinicians to read and reference when designing and reviewing clinical research. It is designed to be a standalone text, written “by a clinician, for clinicians” by a practicing clinical research expert who has had advanced formal training in research methodology, biostatistics, and epidemiology. Topics covered include descriptive and comparative statistics, power and sample size calculations, diagnostic tests, bias, and study design. In each chapter, consideration is given to study mechanics, advantages and disadvantages of each design, and illustrative analytical reviews of existing literature.
Preface
Acknowledgments
About the Author
Foundational Basics
Descriptive Statistics
Introduction
Types of Descriptive Statistics
Measures of Central Tendency
Measures of Variability
Measures of Distribution
Importance of Descriptive Statistics
Conclusion
Reference
Comparative Statistics: Categorical Data
Introduction
What Is Categorical Data?
How Is Categorical Data Best Graphically Represented?
Bivariable Statistics for Categorical Data
Calculating Expected Cell Counts
Post Hoc Pairwise Analyses
Multivariable Statistics for Categorical Data: Logistic Regression
Conclusion
References
Comparative Statistics: Continuous Data
Introduction
What Is Continuous Data?
How Is Continuous Data Best Graphically Represented?
Bivariable Statistics for Continuous Data
Post Hoc Pairwise Analyses
Multivariable Statistics for Continuous Data: Linear Regression
Conclusion
References
Statistical Power and Power Calculations
Introduction
Selecting a Primary Outcome of Interest
Statistical Power and Power Calculations
Post Hoc Power Calculations
Conclusion
References
Characteristics of a Diagnostic Test: Sensitivity, Specificity, Positive Predictive Value, and Negative Predictive Value
Introduction
Sensitivity
Specificity
Positive Predictive Value
Negative Predictive Value
Accuracy Versus Precision
How Are Test Threshold Values Determined? ROC Curves
Reference
Chapter 6: Statistical Bias
Introduction
Selection Bias
Incorporation Bias
Financial Bias
Information Bias
Differential Misclassification
Non-differential Misclassification
Other Types of Information Bias
Publication and Reporting Bias
References
The Iterative Process of Designing Successful Clinical Research
Introduction
Formulating a Study Question
Operationalizing Variables
Directed Acyclic Graphs (DAGs)
Feasibility
Peer and Mentor Review
The Iterative Process
References
Choosing and Executing an Appropriate Clinical Study Design
Randomized Controlled Trials
Study Mechanics
Ideal Scenario for Prospective Randomized Controlled Trial Study Design
Statistical Analysis
Analytical Review
Reference
Case-Control Studies
Study Mechanics
Ideal Scenario for Case-Control Study Design
Statistical Analysis
Analytical Review
Reference
Cohort Studies
Study Mechanics
Ideal Scenario for Cohort Study Design
Cohort Study Patient Selection
Statistical Analysis
Analytical Review
Analytical Review
References
Cross-Sectional Studies
Study Mechanics
Special Circumstances
Ideal Scenario for Cross-Sectional Study Design
Statistical Analysis
Analytical Review
Reference
Case Series and Case Reports
Study Mechanics
Ideal Scenario for Case Series and Case Reports
Statistical Analysis
Analytical Review
Analytical Review
References
Specialized Study Designs
Propensity Score-Matched Studies
Study Mechanics
Ideal Scenario for Propensity Score-Matched Studies
Statistical Analysis
Conclusion
Analytical Review
References
Interrater and Intrarater Reliability Studies
Study Mechanics
Statistical Analysis
Percent Agreement
Kappa
Intraclass Correlation Coefficient
Interpretation of Kappa and Intraclass Correlation Coefficient Values
Analytical Review
References
Clinical Outcome Scale Development and Validation
What Is a Clinical Outcome Scale?
Patient-Reported Outcome Scale Development vs. Cross-Validation of an Existing Scale
Scale Development and Pilot Testing
Structure of Reliability and Validity Testing
Reliability Testing
Construct Validity Testing
Translation and Cross-Cultural Adaptation
Analytical Review
References
Glossary
Index