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Primer of Biostatistics (4th)

by
Edition:
4th
ISBN13:

9780070242685

ISBN10:
0070242682
Format:
Paperback
Pub. Date:
6/1/1996
Publisher(s):
McGraw Hill (Tx)

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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


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