Primer of Biostatistics: Sixth Edition

by Glantz, Stanton A.

• ISBN13: 9780071435093
• ISBN10: 0071435093
• Edition: 6th
• Format: Paperback
• Copyright: 2005-04-15
• Publisher: McGraw-Hill Medical

Summary

Extremely popular, this student-friendly text presents the practical areas of statistics in terms of their relevance to medicine and the life sciences. Includes many illustrative examples and challenging problems that reinforce the author's unique and intuitive approach to the subject. The new edition features a new two-color design, examples taken from current biomedical literature, and review questions within each chapter.

Author Biography

Stanton A. Glantz, PhD: Professor of Medicine, also Member, Cardiovascular Research Institute, Institute for Health Policy Studies, and Cancer Center, University of California, San Francisco.

Table of Contents

Location of Tables for Tests of Significance	xv
Preface	xvii
Biostatistics and Clinical Practice	1	(10)
``Scientific'' Medicine	2	(3)
What Do Statistical Procedures Tell You?	5	(2)
Why Not Depend on the Journals?	7	(2)
Why Has the Problem Persisted?	9	(2)
How to Summarize Data	11	(29)
The Mean	13	(1)
Measures of Variability	14	(1)
The Normal Distribution	15	(1)
Percentiles	16	(5)
Getting the Data	21	(9)
Random Sampling	21	(3)
Bias	24	(2)
Experiments and Observational Studies	26	(3)
Randomized Clinical Trials	29	(1)
How to Estimate the Mean and Standard Deviation from a Sample	30	(1)
How Good Are These Estimates?	31	(7)
Summary	38	(1)
Problems	38	(2)
How to Test for Differences between Groups	40	(33)
The General Approach	41	(5)
Two Different Estimates of the Population Variance	46	(2)
What Is a ``Big'' F?	48	(8)
Three Examples	56	(11)
Glucose Levels in Children of Parents with Diabetes	56	(4)
Halothane versus Morphine for Open-Heart Surgery	60	(4)
Menstrual Dysfunction in Distance Runners	64	(3)
Problems	67	(6)
The Special Case of Two Groups: The t Test	73	(53)
The General Approach	75	(2)
The Standard Deviation of a Difference or a Sum	77	(3)
Use of t to Test Hypotheses about Two Groups	80	(7)
What If the Two Samples Are Not the Same Size?	87	(1)
The Examples Revisited	88	(4)
Glucose Levels in Children of Parents with Diabetes	88	(1)
Halothane versus Morphine for Open-Heart Surgery	88	(4)
The t Test Is an Analysis of Variance	92	(3)
Common Errors in the Use of the t Test and How to Compensate for Them	95	(3)
How to Use t Tests to Isolate Differences between Groups in Analysis of Variance	98	(8)
The Bonferroni t Test	98	(2)
More on Menstruation and Jogging	100	(1)
A Better Approach to Multiple Comparisons: The Holm t Test	101	(3)
The Holm-Sidak Test	104	(2)
Other Approaches to Multiple Comparison Testing: The Student-Newman-Keuls Test	106	(5)
Still More on Menstruation and Jogging	107	(3)
Tukey Test	110	(1)
Which Multiple Comparison Procedure Should You Use?	111	(1)
Multiple Comparisons against a Single Control	112	(5)
Bonferroni t Test	112	(1)
Holm t Test	113	(1)
Dunnett's Test	113	(4)
The Meaning of P	117	(6)
Statistical versus Real (Clinical) Thinking	119	(2)
Why P <.05?	121	(2)
Problems	123	(3)
How to Analyze Rates and Proportions	126	(53)
Back to Mars	127	(5)
Estimating Proportions from Samples	132	(5)
Hypothesis Tests for Proportions	137	(8)
The Yates Correction for Continuity	139	(1)
Mortality Associated with Anesthesia for Open-Heart Surgery with Halothane or Morphine	140	(2)
Prevention of Thrombosis in People Receiving Hemodialysis	142	(3)
Another Approach to Testing Nominal Data: Analysis of Contingency Tables	145	(7)
The Chi-Square Test Statistic	148	(4)
Chi-Square Applications to Experiments with More Than Two Treatments or Outcomes	152	(6)
Subdividing Contingency Tables	155	(3)
The Fisher Exact Test	158	(5)
Measures of Association Between Two Nominal Variables	163	(7)
Prospective Studies and Relative Risk	164	(2)
Case-Control Studies and the Odds Ratio	166	(2)
Passive Smoking and Breast Cancer	168	(2)
Problems	170	(9)
What Does ``Not Significant'' Really Mean?	179	(40)
An Effective Diuretic	180	(5)
Two Types of Errors	185	(1)
What Determines a Test's Power?	186	(18)
The Size of the Type I Error α	187	(6)
The Size of the Treatment Effect	193	(2)
The Population Variability	195	(2)
Bigger Samples Mean More Powerful Tests	197	(4)
What Determines Power? A Summary	201	(2)
Another Look at Halothane versus Morphine for Open-Heart Surgery	203	(1)
Power and Sample Size for Analysis of Variance	204	(4)
Power, Menstruation, and Running	206	(2)
Power and Sample Size for Comparing Two Proportions	208	(3)
Mortality Associated with Anesthesia for Open-Heart Surgery	210	(1)
Sample Size for Comparing Two Proportions	211	(1)
Power and Sample Size for Relative Risk and Odds Ratio	211	(1)
Power and Sample Size for Contingency Tables	212	(2)
Physicians, Perspiration, and Power	213	(1)
Practical Problems in Using Power	214	(1)
What Difference Does It Make?	215	(3)
Problems	218	(1)
Confidence Intervals	219	(34)
The Size of the Treatment Effect Measured as the Difference of Two Means	220	(3)
The Effective Diuretic	223	(4)
More Experiments	224	(3)
What Does ``Confidence'' Mean?	227	(2)
Confidence Intervals Can Be Used to Test Hypotheses	229	(2)
Confidence Interval for the Population Mean	231	(2)
The Size of the Treatment Effect Measured as the Difference of Two Rates or Proportions	233	(7)
Difference in Mortality Associated with Anesthesia for Open-Heart Surgery	234	(1)
Difference in Thrombosis with Aspirin in People Receiving Hemodialysis	235	(1)
How Negative Is a ``Negative'' Clinical Trial?	236	(1)
Meta-analysis	236	(4)
Confidence Interval for Rates and Proportions	240	(5)
Quality of Evidence Used as a Basis for Interventions to Improve Hospital Antibiotic Prescribing	241	(1)
Exact Confidence Intervals for Rates and Proportions	242	(3)
Confidence Intervals for Relative Risk and Odds Ratio	245	(2)
Difference in Thrombosis with Aspirin in People Receiving Hemodialysis	246	(1)
Passive Smoking and Breast Cancer	247	(1)
Confidence Interval for the Entire Population	247	(4)
Problems	251	(2)
How to Test for Trends	253	(68)
More about the Martians	254	(5)
The Population Parameters	257	(2)
How to Estimate the Trend from a Sample	259	(19)
The Best Straight Line through the Data	259	(8)
Variability about the Regression Line	267	(1)
Standard Errors of the Regression Coefficients	268	(4)
How Convincing Is the Trend?	272	(2)
Confidence Interval for the Line of Means	274	(1)
Confidence Interval for an Observation	275	(3)
How to Compare Two Regression Lines	278	(7)
Overall Test for Coincidence of Two Regression Lines	280	(1)
Relationship between Weakness and Muscle Wasting in Rheumatoid Arthritis	281	(4)
Correlation and Correlation Coefficients	285	(11)
The Pearson Product-Moment Correlation Coefficient	287	(3)
The Relationship between Regression and Correlation	290	(2)
How to Test Hypotheses about Correlation Coefficients	292	(1)
Journal Size and Selectivity	293	(3)
The Spearman Rank Correlation Coefficient	296	(6)
Variation among Interns in Use of Laboratory Tests: Relation to Quality of Care	300	(2)
Power and Sample Size in Regression and Correlation	302	(3)
Comparing Two Different Measurements of the Same Thing: The Bland-Altman Method	305	(5)
Assessing Mitral Regurgitation with Echocardiography	306	(4)
Summary	310	(1)
Problems	310	(11)
Experiments When Each Subject Receives More than One Treatment	321	(42)
Experiments When Subjects Are Observed before and after a Single Treatment: The Paired t Test	322	(8)
Cigarette Smoking and Platelet Function	325	(5)
Another Approach to Analysis of Variance	330	(12)
Some New Notation	331	(8)
Accounting for All the Variability in the Observations	339	(3)
Experiments When Subjects Are Observed after Many Treatments: Repeated-Measures Analysis of Variance	342	(12)
Anti-Asthmatic Drugs and Endotoxin	348	(4)
How to Isolate Differences in Repeated-Measures Analysis of Variance	352	(1)
Power in Repeated-Measures Analysis of Variance	353	(1)
Experiments When Outcomes Are Measured on a Nominal Scale: McNemar's Test	354	(3)
p7 Antigen Expression in Human Breast Cancer	354	(3)
Problems	357	(6)
Alternatives to Analysis of Variance and the t Test Based on Ranks	363	(50)
How to Choose between Parametric and Nonparametric Methods	364	(3)
Two Different Samples: The Mann-Whitney Rank-Sum Test	367	(11)
The Leboyer Approach to Childbirth	374	(4)
Each Subject Observed before and after One Treatment: The Wilcoxon Signed-Rank Test	378	(8)
Cigarette Smoking and Platelet Function	385	(1)
Experiments with Three or More Groups When Each Group Contains Different Individuals: The Kruskal-Wallis Statistic	386	(9)
Prenatal Marijuana Exposure and Child Behavior	389	(1)
Nonparametric Multiple Comparisons	390	(2)
More on Marijuana	392	(3)
Experiments in Which Each Subject Receives More than One Treatment: The Friedman Test	395	(10)
Anti-Asthmatic Drugs and Endotoxin	399	(2)
Multiple Comparisons after Friedman's Test	401	(1)
Effect of Secondhand Smoke on Angina Pectoris	401	(4)
Summary	405	(1)
Problems	406	(7)
How to Analyze Survival Data	413	(31)
Censoring on Pluto	414	(3)
Estimating the Survival Curve	417	(10)
Median Survival Time	423	(1)
Standard Errors and Confidence Limits for the Survival Curve	424	(3)
Comparing Two Survival Curves	427	(10)
Bone Marrow Transplantation to Treat Adult Leukemia	429	(7)
The Yates Correction for the Log-Rank Test	436	(1)
Gehan's Test	437	(1)
Power and Sample Size	438	(2)
Summary	440	(1)
Problems	440	(4)
What Do the Data Really Show?	444	(22)
When to Use Which Test	445	(2)
Randomize and Control	447	(8)
Internal Mammary Artery Ligation to Treat Angina Pectoris	448	(1)
The Portacaval Shunt to Treat Cirrhosis of the Liver	449	(3)
Is Randomization of People Ethical?	452	(2)
Is a Randomized Controlled Trial Always Necessary?	454	(1)
Does Randomization Ensure Correct Conclusions?	455	(5)
Problems with the Population	460	(2)
How You Can Improve Things	462	(4)
Appendix A Computational Forms	466	(6)
Appendix B Power Charts	472	(9)
Appendix C Answers to Exercises	481	(16)
Index	497