Clinical Biostatistics and Epidemiology Made Ridiculously Simple: An Incredibly Easy Way to Learn for Medical, Nursing, PA Students, And Other Healthcare Professionals (MedMaster Medical Books)

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

This book is wonderful. Without it I would not have passed biostatistics I. I don't even read my course textbook, I relied completely on this book and scored a "B" on my final. Several of my classmates also purchased this book and feel the same way as I do. Our course director is actually going to incorporate this book into the course next year. It is a short read, roughly 90 pages. The nice thing about it is you can look up the topic in the index and skip around based upon what concept you are trying to understand. The authors do a phenomenal job of breaking down concepts with minimal math. I appreciate this as incorporating math makes it way more confusing. I want to understand the fundamental concepts first.

It took until I retired from being a medical educator to finally find a text that, indeed, makes clinical biostatistics simple for the medical provider, medical student, physician resident, health sciences student, and medical educator. If I were still in the medical center, the lab or the classroom, this wonderfully short and to-the-point text would be in my briefcase for immediate reference. It's become essential for medical professionals to possess biostatistical knowledege and skills that will enable them to evaluate current research and reported "exciting new findings." The ability to adequately evaluate the merits of a study's design and its conclusions is as much a part of being a fine physician as is any other provider skill. We have long been in need of a handbook that makes clinical biostatistics ridiculously simple; Drs Weaver and Goldberg have now published such a text.

This book is great. I just started a literature review course, and I was feeling a little overwhelmed by all the biostatistical tests that were being done in all the trials that we were reviewing. I had taken a biostats course, but I barely understood was going on during that time as well. This book gave a simple explanation for most of the tests that were being discussed in the studies and helped me to better understand why the test was being done and what each test was used for.

I recommend this book for someone who needs a really simplified explanation of biostats. As the title of the book implies, the author really makes biostats seem ridiculously simple.

This is a broad, concise, and laconic overview of clinical biostats. The topics covered in the 95 pages include probability, research design, statistical testing, and regression analysis. In addition, the reader is supported with z-tables, t-tables, and common abbreviations.

Ms Weaver includes a clever section on critical assessment of clinical trial data.
Adeptly written and highly recommended as a quick guide.

Not enough Biostats for med school =(. Class wanted way more info than taught but book covered basics really well.

I thought this book was terrible for what I needed it for. I'm taking biostats and my prof is awful, so I was trying to teach myself. Maybe it's a great book for interpreting stats, but not for calculating.

Good book, but was not really what I was looking for. If you want a really "basic" knowledge of Clinical Biostats, then this book is for you. If not, look for a more advanced copy out there. I purchased this book to aid me in teaching a Healthcare Statistics class. I found the majority of the book to be really "basic", not just "simple". If that makes sense!

I am in a graduate program that deals with bioimaging and relevant statistical procedures. As an undergrad, I got straight easy "A's" in a high level stat class (for sociology, using SPSS). However, this new course presented formulas and symbols that I thought I already knew, and and I lost sight of the fundamental and simple principles underlying all stats

This got me back on track and grounded in the basics in a day or two. *To note again: this book's scope is not going to teach intricate formulas, etc.

Thank you!

This is an extremely well written summary of the key concepts in biostat and epi. If you have very limited time to learn the vocabulary and key concepts this is an excellent study guide. Because there are multiple paragraphs on the key ideas, it is a bit of an injustice to call this an extremely good, well organized, glossary (or *very* abridged encyclopedia) rather than a traditional textbook, but that is not far off the mark. You will not get extended stories to teach you the materials but you will get clear explanations and some nice mnemonics to help you remember the vocabulary.