Primer of Biostatistics (4th)

by Glantz, Stanton A.

• ISBN13: 9780070242685
• ISBN10: 0070242682
• Edition: 4th
• Format: Paperback
• Copyright: 1996-06-01
• Publisher: McGraw Hill (Tx)

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.

Table of Contents

Preface	xiii
Biostatistics and Clinical Practice	1	(10)
The Changing Medical Environment	1	(3)
What Do Statistical Procedures Tell You?	4	(2)
Why Not Depend on the Journals?	6	(3)
Why Has the Problem Persisted?	9	(2)
How to Summarize Data	11	(21)
The Mean	13	(1)
Measures of Variability	14	(1)
The Normal Distribution	15	(1)
Percentiles	16	(5)
How to Make Estimates from a Limited Sample	21	(1)
How Good Are These Estimates?	22	(7)
Summary	29	(3)
How to Test for Differences between Groups	32	(33)
The General Approach	32	(5)
Two Different Estimates of the Population Variance	37	(2)
What Is a "Big" F?	39	(8)
Three Examples	47	(18)
Relationship of Drug Prescribing to Length of Hospital Stay	48	(4)
Halothane versus Morphine for Open-Heart Anesthesia	52	(5)
Menstrual Dysfunction in Distance Runners	57	(8)
The Special Case of Two Groups: The t Test	65	(43)
The General Approach	67	(2)
The Standard Deviation of a Difference or a Sum	69	(3)
Use of t to Test Hypotheses about Two Groups	72	(7)
What If the Two Samples Are Not the Same Size?	79	(2)
The Examples Revisited	81	(3)
Relationship of Drug Prescribing to Length of Hospital Stay	82	(1)
Halothane versus Morphine for Open-Heart Surgery	82	(2)
The t Test Is an Analysis of Variance	84	(2)
Common Errors in the Use of the t Test and How to Compensate for Them	86	(3)
How to Use t Tests to Isolate Differences between Groups in Analysis of Variance	89	(3)
More on Menstruation and Jogging	91	(1)
A Better Approach to Multiple Comparison Testing: The Student-Newman-Keuls Test	92	(6)
Still More on Menstruation and Jogging	94	(3)
The Tukey Test	97	(1)
Multiple Comparisons against a Single Control	98	(5)
Bonferroni t Tests	98	(1)
Dunnett's Test	99	(4)
The Meaning of P	103	(5)
How to Analyze Rates and Proportions	108	(43)
Back to Mars	109	(4)
Estimating Proportions from Samples	113	(6)
Hypothesis Tests for Proportions	119	(8)
The Yates Correction for Continuity	121	(1)
Mortality Associated with Anesthesia for Open-Heart Surgery with Halothane or Morphine	122	(2)
Prevention of Thrombosis in People Receiving Hemodialysis	124	(3)
Another Approach to Testing Nominal Data: Analysis of Contingency Tables	127	(7)
The Chi-Square Test Statistic	130	(4)
Chi-Square Applications to Experiments with More Than Two Treatments or Outcomes	134	(6)
Subdividing Contingency Tables	137	(3)
The Fisher Exact Test	140	(11)
What Does "Not Significant" Really Mean?	151	(33)
An Effective Diuretic	152	(4)
Two Types of Errors	156	(2)
What Determines a Test's Power?	158	(13)
The Size of the Type I Error α	158	(3)
The Size of the Treatment Effect	161	(2)
The Population Variability	163	(2)
Bigger Samples Mean More Powerful Tests	165	(2)
What Determines Power? A Summary	167	(3)
Another Look at Halothane versus Morphine for Open-Heart Surgery	170	(1)
Power and Sample Size Computations for Analysis of Variance	171	(4)
Power, Menstruation, and Running	174	(1)
Power and Sample Size for Contingency Tables	175	(2)
Physicians, Perspiration, and Power	176	(1)
Practical Problems in Using Power	177	(1)
What Difference Does It Make?	178	(6)
Confidence Intervals	184	(29)
The Size of the Treatment Effect Measured as the Difference of Two Means	185	(3)
The Effective Diuretic	188	(1)
More Experiments	189	(3)
What Does "Confidence" Mean?	192	(2)
Confidence Intervals Can Be Used to Test Hypotheses	194	(3)
Confidence Interval for the Population Mean	197	(4)
The Size of the Treatment Effect Measured as the Difference of Two Rates or Proportions	198	(1)
Difference in Mortality Associated with Anesthesia for Open-Heart Surgery	199	(1)
Difference in Thrombosis with Aspirin in People Receiving Hemodialysis	200	(1)
How Negative Is a "Negative" Clinical Trial?	201	(1)
Confidence Interval for Rates and Proportions	201	(6)
The Fraction of Articles with Statistical Erros	203	(1)
Exact Confidence Intervals for Rates and Proportions	204	(3)
Confidence Interval for the Entire Population	207	(6)
How to Test for Trends	213	(69)
More about the Martians	214	(5)
The Population Parameters	217	(2)
How to Estimate the Trend from a Sample	219	(19)
The Best Straight Line through the Data	219	(7)
Variability about the Regression Line	226	(2)
Standard Errors of the Regression Coefficients	228	(4)
How Convincing Is the Trend?	232	(2)
Confidence Interval for the Regression Line	234	(1)
Confidence Interval for an Observation	235	(3)
How to Compare Two Regression Lines	238	(7)
Overall Test for Coincidence of Two Regression Lines	239	(2)
Relationship between Weakness and Muscle Wasting in Rheumatoid Arthritis	241	(4)
Correlation and Correlation Coefficients	245	(11)
The Pearson Product-Moment Correlation Coefficient	247	(2)
The Relationship between Regression and Correlation	249	(3)
How to Test Hypotheses about Correlation Coefficients	252	(1)
Dietary Fat and Breast Cancer	253	(3)
The Spearman Rank Correlation Coefficient	256	(6)
Variation among Interns in Use of Laboratory Tests: Relation to Quality of Care	259	(3)
Power and Sample Size in Regression and Correlation	262	(4)
Comparing Two Different Measurements of the Same Thing: The Bland-Altman Method	266	(5)
Assessing Mitral Regurgitation with Echocardiography	267	(4)
Summary	271	(11)
Experiments When Each Subject Receives More than One Treatment	282	(41)
Experiments When Subjects Are Observed before and after a Single Treatment: The Paired t Test	283	(8)
Cigarette Smoking and Platelet Function	286	(5)
Another Approach to Analysis of Variance	291	(11)
Some New Notation	292	(7)
Accounting for All the Variability in the Observation	299	(3)
Experiments When Subjects Are Observed after many Treatments: Repeated-Measures Analysis of Variance	302	(12)
Oral Hydralazine Therapy for Pulmonary Hypertension	308	(5)
How to Isolate Differences in Repeated-Measures Analysis of Variance	313	(1)
Power in Repeated-Measures Analysis of Variance	314	(1)
Experiments When Outcomes Are Measured on a Nominal Scale: McNemar's Test	314	(9)
Skin Reactivity in People with Cancer	314	(9)
Alternatives to Analysis of Variance and the t Test Based on Ranks	323	(50)
How to Choose between Parametric and Nonparametric Methods	324	(3)
Two Different Samples: The Mann-Whitney Rank-Sum Test	327	(11)
The Leboyer Approach to Childbirth	333	(5)
Each Subject Observed before and after One Treatment: The Wilcoxon Signed-Rank Test	338	(8)
Cigarette Smoking and Platelet Function	345	(1)
Experiments with Three or More Groups When Each Group Contains Different Individuals: The Kruskal-Wallis Statistic	346	(9)
Elimination of Caffeine Impaired by Oral Contraceptives	349	(1)
Nonparametric Multiple Comparisons	350	(3)
Another Dose of Caffeine	353	(2)
Experiments in Which Each Subject Receives More than One Treatment: The Friedman Test	355	(11)
Oral Hydralazine Therapy for Primary Pulmonary Hypertension	360	(1)
Multiple Comparisons after Friedman's Test	361	(1)
Effect of Secondhand Smoke on Angina Pectoris	362	(4)
Summary	366	(7)
How to Analyze Survival Data	373	(30)
Censoring on Pluto	374	(3)
Estimating the Survival Curve	377	(10)
Median Survial Time	384	(1)
Standard Errors and Confidence Limits for the Survival Curve	384	(3)
Comparing Two Survival Curves	387	(9)
Bone Marrow Transplantation to Treat Adult Leukemia	389	(7)
The Yates Correction for the Log-Rank Test	396	(1)
Gehan's Test	396	(2)
Power and Sample Size	398	(1)
Summary	399	(4)
What Do the Data Really Show?	403	(22)
When to Use Which Test	404	(2)
Randomize and Control	406	(8)
Internal Mammary Artery Ligation to Treat Angina Pectoris	407	(1)
The Portacaval Shunt to Treat Cirrhosis of the Liver	408	(3)
Is Randomization of People Ethical?	411	(2)
Is a Randomized Controlled Trial Always Necessary?	413	(1)
Does Randomization Ensure Correct Conclusions?	414	(5)
Problems with the Population	419	(2)
How You Can Improve Things	421	(4)
Appendixes	425	(30)
A Computational Forms	425	(6)
B Power Charts	431	(9)
C Answers to Exercises	440	(15)
Index	455