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Primer of Biostatistics, 7th Edition.pdf
Contents
Preface
1. Biostatistics and Clinical Practice
What Do Statistical Procedures Tell You?
Why Not Just Depend on the Journals?
Why Has the Problem Persisted?
2. How to Summarize Data
Three Kinds of Data
The Mean
Measures of Variability
The Normal Distribution
Getting the Data
Random Sampling
Bias
Experiments and Observational Studies
Randomized Clinical Trials
How to Estimate the Mean and Standard Deviation from a Sample
How Good Are These Estimates?
Percentiles
Pain Associated with Diabetic Neuropathy
Summary
Problems
3. How to Test for Differences between Groups
The General Approach
Two Different Estimates of the Population Variance
What is a “Big” F?
Cell Phones and Sperm
An Early Study
A Better Control Group
An Experimental Study
Unequal Sample Size
Two Way Analysis of Variance
Problems
4. The Special Case of Two Groups: The t test
The General Approach
The Standard Deviation of a Difference or a Sum
Use of t to test Hypotheses About Two Groups
What if the Two Samples Are Not the Same Size?
Cell Phones Revisited
The t test is an Analysis of Variance
Common Errors in the Use of the t test and How to Compensate for Them
How to Use t tests to Isolate Differences Between Groups in Analysis of Variance
The Bonferroni t test
More on Cell Phones and Rabbit Sperm
A Better Approach to Multiple Comparisons: The Holm t test
The Holm-Sidak t test
Multiple Comparisons Against a Single Control
The Meaning of P
Statistical Versus Real (Clinical) Thinking
Why P < .05?
Problems
5. How to Analyze Rates and Proportions
Back to Mars
Estimating Proportions from Samples
Hypothesis Tests for Proportions
The Yates Correction for Continuity
Effect of Counseling on End-of-Life Planning in Homeless People
Another Approach to Testing Nominal Data: Analysis of Contingency Tables
The Chi-Square Test Statistic
The Yates Correction for Continuity
Chi-Square Applications to Experiments with More than Two Treatments or Outcomes
Multiple Comparisons
The Fisher Exact Test
Measures of Association Between Two Nominal Variables
Prospective Studies and Relative Risk
Absolute Risk Increase (or Reduction) and Number Needed to Treat
Case-Control Studies and the Odds Ratio
Passive Smoking and Breast Cancer
Problems
6. What Does “Not Significant” Really Mean?
An Effective Diuretic
Two Types of Errors
What Determines a Test’s Power?
The Size of the Type I Error, α
The Size of the Treatment Effect
The Population Variability
Bigger Samples Mean More Powerful Tests
What Determines Power? A Summary
Muscle Strength in People with Chronic Obstructive Pulmonary Disease
Power and Sample Size for Analysis of Variance
Power and Sperm Motility
Power and Sample Size for Comparing Two Proportions
Power and Polyethylene Bags
Sample Size for Comparing Two Proportions
Power and Sample Size for Relative Risk and Odds Ratio
Power and Sample Size for Contingency Tables
Power and Polyethylene Bags (Again)
Practical Problems in Using Power
What Difference Does it Make?
Problems
7. Confidence Intervals
The Size of the Treatment Effect Measured as the Difference of Two Means
The Effective Diuretic
More Experiments
What Does “Confidence” Mean?
Confidence Intervals Can Be Used to Test Hypotheses
Confidence Interval for the Population Mean
The Size of the Treatment Effect Measured as the Difference of Two Rates or Proportions
Difference in Survival for Two Methods for Keeping Extremely Low Birth Weight Infants Warm
How Negative Is a “Negative” Clinical Trial?
Meta-Analysis
Confidence Interval for Rates and Proportions
Quality of Evidence Used as a Basis for Interventions to Improve Hospital Antibiotic Prescribing
Exact Confidence Intervals for Rates and Proportions
Confidence Intervals for Relative Risk and Odds Ratio
Effect of Counseling on Filing Advance Directives for End of Life Care Among Homeless People
Passive Smoking and Breast Cancer
Confidence Interval for the Entire Population
Problems
8. How to Test for Trends
More About the Martians
The Population Parameters
How to Estimate the Trend from a Sample
The Best Straight Line through the Data
Variability about the Regression Line
Standard Errors of the Regression Coefficients
How Convincing Is the Trend?
Confidence Interval for the Line of Means
Confidence Interval for an Observation
Cell Phone Radiation, Reactive Oxygen Species, and DNA Damage in Human Sperm
How to Compare Two Regression Lines
Overall Test for Coincidence of Two Regression Lines
Relationship between Weakness and Muscle Wasting in Rheumatoid Arthritis
Correlation and Correlation Coefficients
The Pearson Product-Moment Correlation Coefficient
The Relationship Between Regression and Correlation
How to Test Hypotheses about Correlation Coefficients
Journal Size and Selectivity
The Spearman Rank Correlation Coefficient
Cell Phone Radiation and Mitochondrial Reactive Oxygen Species in Sperm
Power and Sample Size in Regression and Correlation
Comparing Two Different Measurements of the Same Thing: The Bland-Altman Method
Assessing Mitral Regurgitation with Echocardiography
Multiple Regression
Summary
Problems
9. Experiments When Each Subject Receives More Than One Treatment
Experiments When Subjects Are Observed Before and After a Single Treatment: the Paired t test
Cigarette Smoking and Platelet Function
Another Approach to Analysis of Variance
Some New Notation
Accounting for All the Variability in the Observations
Experiments When Subjects Are Observed After Many Treatments: Repeated Measures Analysis of Variance
Anti-asthmatic Drugs and Endotoxins
How to Isolate Differences in Repeated Measures Analysis of Variance
Power in Repeated Measures Analysis of Variance
Experiments When Outcomes Are Measured on a Nominal Scale: McNemar’s Test
p7 Antigen Expression in Human Breast Cancer
Problems
10. Alternatives to Analysis of Variance and the t test Based on Ranks
How to Choose Between Parametric and Nonparametric Methods
Two Different Samples: The Mann-Whitney Rank-Sum test
Use of a Cannabis-Based Medicine in Painful Diabetic Neuropathy
Each Subject Observed Before and After One Treatment: The Wilcoxon Signed-Rank Test
Cigarette Smoking and Platelet Function
Experiments with Three or More Groups When Each Group Contains Different Individuals: The Kruskal-Wallis test
Prenatal Marijuana Exposure and Child Behavior
Nonparametric Multiple Comparisons
Experiments in Which Each Subject Receives More than One Treatment: The Friedman Test
Anti-asthmatic Drugs and Endotoxin
Multiple Comparisons After the Friedman Test
Summary
Problems
11. How to Analyze Survival Data
Censoring on Pluto
Estimating the Survival Curve
Median Survival Time
Standard Errors and Confidence Limits for the Survival Curve
Comparing Two Survival Curves
Bone Marrow Transplantation to Treat Adult Leukemia
The Yates Correction for the Log Rank Test
Gehan’s Test
Power and Sample Size
Power
Sample Size
Summary
Problems
12. What Do the Data Really Show?
Cell Phones: Putting All the Pieces Together
When to Use Which Test
Issues in Study Design
Randomize and Control
Internal Mammary Artery Ligation to Treat Angina Pectoris
The Portacaval Shunt to Treat Cirrhosis of the Liver
Is Randomization of People Ethical?
Is a Randomized Controlled Trial Always Necessary?
Does Randomization Ensure Correct Conclusions?
Problems with the Population
How You Can Improve Things
Appendix A. Computational Forms
To Interpolate Between Two Values in a Statistical Table
Variance
One-Way Analysis of Variance
Given Sample Means and Standard Deviations
Given Raw Data
Unpaired t Test
Given Sample Means and Standard Deviations
Given Raw Data
2 × 2 Contingency Tables (Including Yates Correction for Continuity)
Chi Square
McNemar’s Test
Fisher Exact Test
Linear Regression and Correlation
Repeated Measures Analysis of Variance
Kruskal–Wallis Test
Friedman Test
Appendix B. Statistical Tables and Power Charts
Statistical Tables
Critical Values of F Corresponding to P < .05 and P < .01
Critical Values of t (Two-Tailed)
Holm-Sidak Critical P Values for Individual Comparisons to Maintain a 5% Family Error Rate (α[sub(T)] = .05)
Critical Values for the χ[sup(2)] Distribution
Critical Values of t (One-Tailed)
Critical Values for Spearman Rank Correlation Coefficient
Critical Values (Two-Tailed) of the Mann-Whitney Rank-Sum T
Critical Values (Two-Tailed) of Wilcoxon W
Critical Values for Friedman χ[sup(2)][sub(r)]
Power Charts for Analysis of Variance
Appendix C. Answers to Exercises
Index
A
B
C
D
E
F
G
H
I
K
L
M
N
O
P
Q
R
S
T
U
V
W
Y
Z
Primer of Biostatistics
Notice
Medicine is an ever-changing science. As new research and clinical experience broaden
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made in the recommended dose or in the contraindications for administration. This
recommendation is of particular importance in connection with new or infrequently
used drugs.
Primer of Biostatistics
Seventh Edition
Stanton A. Glantz, PhD
Professor of Medicine
American Legacy Foundation Distinguished Professor in Tobacco Control
Director, Center for Tobacco Control Research and Education
Member, Cardiovascular Research Institute
Member, Philip R. Lee Institute for Health Policy Studies
Member, Helen Diller Family Comprehensive Cancer Center
University of California, San Francisco
San Francisco, California
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To Marsha Kramar Glantz
This page intentionally left blank
What I’ve proposed is that we have a panel of medical experts that are making determina-
tions about what protocols are appropriate for what diseases. There’s going to be some
disagreement, but if there’s broad agreement that, in this situation the blue pill works better
than the red pill, and it turns out the blue pills are half as expensive as the red pill, then we
want to make sure that doctors and patients have that information available to them.
President Barack Obama, 2009*
*Interview with ABC News’ Dr. Timothy Johnson, July 15, 2009.
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