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Primer of Biostatistics (4th),9780070242685

Primer of Biostatistics (4th)

by Glantz, Stanton A.

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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