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The most important points in clinical biostatistics, presented intuitively with clinical examples. Because intuitive concepts are the easiest to learn and retain, this book minimizes math and emphasizes concepts. From terminology to research design to various statistical testing, this text provides a lasting clear approach to interpreting medical research reports. Valuable for biostatistics courses.
Excellent USMLE Board Review.
Contents
Book Details
| Pages | 104 |
|---|---|
| ISBN | 9781935660026 |
| Publication | Edition 1 (April 1, 2012) |
| Language | English |
TABLE OF CONTENTS
PART I. INTRODUCTION
CHAPTER 1. TERMINOLOGY
Population, Sample, and Element
Descriptive vs. Inferential Statistics
Parameter vs. Statistic
Sampling Error vs. Selection Bias
Imprecision vs. Bias (Inaccuracy)
Validity vs. Reliability
Independent vs. Dependent Variables
Normal (Gaussian), Skewed and Kurtotic curves
Multiplication and Addition Rules of Probability
Statistical Significance vs. Clinical Significance
Statistical Abnormality vs. Clinical Abnormality
CHAPTER 2. MEAN, MEDIAN, AND MODE
Mean
Median
Mode
What’s Wrong Here? *#!!
CHAPTER 3. RANGE, VARIATION, AND STANDARD DEVIATION
Range
Variance
Standard Deviation
Coefficient of Variation
CHAPTER 4. KINDS OF DATA
Nominal Data
Ordinal Data
Interval Data
Ratio Data
PART II. RESEARCH DESIGN
CHAPTER 5. KINDS OF STUDIES
Randomized Control Studies
Matching Studies
Stratified Randomization Studies
Blind Studies
Prospective (Cohort; Longitudinal) Studies
Retrospective (Case-control) Studies
Cross-sectional (Prevalence) Studies
Experimental vs. Observational Studies
Case Series and Case Reports
Meta-analysis
Crossover, Between-subjects, and Within-subjects Studies
Therapeutic Trials
CHAPTER 6. GRAPHING
Bar Graphs (Bar Charts)
Tables
Histograms
Line Graphs
Cumulative Frequency Curves
Box-and-Whiskers Plots
Stem-and-Leaf Plots
Scattergrams
Survival Curves
CHAPTER 7. HYPOTHESIS TESTING
The Null and Alternative Hypotheses
Rejecting the Null Hypothesis
PART III. STATISTICAL TESTS
Parametric vs. Nonparametric Tests
CHAPTER 8. DESCRIPTIVE STATISTICS
The Z-score
CHAPTER 9. INFERENTIAL STATISTICS
Confidence Intervals vs. Hypothesis Testing and P-values
CHAPTER 10. STANDARD ERROR OF THE MEAN
The Central Limit Theorem
Standard Error of the Mean (SEM)
CHAPTER 11. THE T-TEST
The Meaning of the T-Test
Comparing Two Samples
CHAPTER 12. ONE-TAILED VS. TWO-TAILED STUDIES
CHAPTER 13. P-ING (PEE-ING) ALL OVER THE PLACE
CHAPTER 14. TYPE I AND TYPE II ERRORS AND POWER
Type I and Type II Errors
Power
Effect Size
Bayesian Thinking
Calculation of Sample Size
CHAPTER 15. ANOVA (ANALYSIS OF VARIANCE)
ANOVA and F-ratio
MANOVA and ANCOVA
CHAPTER 16. CORRELATION AND REGRESSION
Correlation Techniques
Correlation Coefficient
Coefficient of Determination
Correlation Does Not Mean Causation
Criteria of Causality
Regression
Kinds of Regression Analysis
Regression to the Mean
CHAPTER 17. NONPARAMETRIC TESTS
Chi Square Goodness-of-Fit Test
Nonparametric Tests That Use Ranking
Nonparametric Tests That Do Not Use Ranking
CHAPTER 18. EPIDEMIOLOGICAL TESTS
Incidence vs. Prevalence
Mortality, Morbidity, and Case Fatality
Absolute Risk vs. Relative Risk (RR)
Odds and Odds Ratio (Relative Odds)
Absolute Risk Reduction (Attributable Risk) vs. Relative Risk Reduction
Number Needed to Treat (NNT)
Number Needed to Harm (NNH)
Sensitivity vs. Specificity
Positive and Negative Predictive Values
PART IV. ARE THE RESEARCH CONCLUSIONS CORRECT?
CHAPTER 19. WHAT’S WRONG HERE? *#!!
Who Says So?
How Does the Researcher Know?
What’s Missing?
Did Someone Change the Subject?
Does It Make Sense?
Appendix A. The Z table
Appendix B. The T table
Appendix C. The Chi-square Table
References
INDEX
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