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Applied Medical Statistics

Jingmei Jiang (Author)

9781119716709, Wiley

Hardback, published 29 April 2022

640 pages
25.4 x 17.8 x 3.2 cm, 1.222 kg

APPLIED MEDICAL STATISTICS

An up-to-date exploration of foundational concepts in statistics and probability for medical students and researchers

Medical journals and researchers are increasingly recognizing the need for improved statistical rigor in medical science. In Applied Medical Statistics, renowned statistician and researcher Dr. Jingmei Jiang delivers a clear, coherent, and accessible introduction to basic statistical concepts, ideal for medical students and medical research practitioners. The book will help readers master foundational concepts in statistical analysis and assist in the development of a critical understanding of the basic rationale of statistical analysis techniques.

The distinguished author presents information without assuming the reader has a background in specialized mathematics, statistics, or probability. All of the described methods are illustrated with up-to-date examples based on real-world medical research, supplemented by exercises and case discussions to help solidify the concepts and give readers an opportunity to critically evaluate different research scenarios.

Readers will also benefit from the inclusion of:

  • A thorough introduction to basic concepts in statistics, including foundational terms and definitions, location and spread of data distributions, population parameters estimation, and statistical hypothesis tests
  • Explorations of commonly used statistical methods, including t-tests,analysis of variance, and linear regression
  • Discussions of advanced analysis topics, including multiple linear regression and correlation, logistic regression, and survival analysis
  • Substantive exercises and case discussions at the end of each chapter

Perfect for postgraduate medical students, clinicians, and medical and biomedical researchers, Applied Medical Statistics will also earn a place on the shelf of any researcher with an interest in biostatistics or applying statistical methods to their own field of research.

Preface xiii

Acknowledgments xv

About the Companion Website xvii

1 What is Biostatistics 1

1.1 Overview 1

1.2 Some Statistical Terminology 2

1.3 Workflow of Applied Statistics 6

1.4 Statistics and Its Related Disciplines 6

1.5 Statistical Thinking 7

1.6 Summary 7

1.7 Exercises 8

2 Descriptive Statistics 11

2.1 Frequency Tables and Graphs 12

2.2 Descriptive Statistics of Numerical Data 17

2.3 Descriptive Statistics of Categorical Data 31

2.4 Constructing Statistical Tables and Graphs 38

2.5 Summary 47

2.6 Exercises 48

3 Fundamentals of Probability 53

3.1 Sample Space and Random Events 54

3.2 Relative Frequency and Probability 58

3.3 Conditional Probability and Independence of Events 60

3.4 Multiplication Law of Probability 61

3.5 Addition Law of Probability 62

3.6 Total Probability Formula and Bayes’ Rule 63

3.7 Summary 65

3.8 Exercises 65

4 Discrete Random Variable 69

4.1 Concept of the Random Variable 69

4.2 Probability Distribution of the Discrete Random Variable 70

4.3 Numerical Characteristics 73

4.4 Commonly Used Discrete Probability Distributions 75

4.5 Summary 87

4.6 Exercises 87

5 Continuous Random Variable 91

5.1 Concept of Continuous Random Variable 92

5.2 Numerical Characteristics 93

5.3 Normal Distribution 94

5.4 Application of the Normal Distribution 102

5.5 Summary 109

5.6 Exercises 110

6 Sampling Distribution and Parameter Estimation 113

6.1 Samples and Statistics 114

6.2 Sampling Distribution of a Statistic 114

6.3 Estimation of One Population Parameter 124

6.4 Estimation of Two Population Parameters 132

6.5 Summary 141

6.6 Exercises 141

7 Hypothesis Testing for One Parameter 145

7.1 Overview 145

7.2 Hypothesis Testing for One Parameter 155

7.3 Further Considerations on Hypothesis Testing 164 

7.4 Summary 165

7.5 Exercises 166

8 Hypothesis Testing for Two Population Parameters 169

8.1 Testing the Difference Between Two Population Means: Paired Samples 170

8.2 Testing the Difference Between Two Population Means: Independent Samples 173

8.3 Testing the Difference Between Two Population Rates (Normal Approximation Method) 185

8.4 Summary 188

8.5 Exercises 189

9 One-way Analysis of Variance 193

9.1 Overview 193

9.2 Procedures of ANOVA 196

9.3 Multiple Comparisons of Means 204

9.4 Checking ANOVA Assumptions 211

9.5 Data Transformations 217

9.6 Summary 218

9.7 Exercises 218

10 Analysis of Variance in Different Experimental Designs 221

10.1 ANOVA for Randomized Block Design 221

10.2 ANOVA for Two-factor Factorial Design 229

10.3 ANOVA for Repeated Measures Design 240

10.4 ANOVA for 2 × 2 Crossover Design 251

10.5 Summary 256

10.6 Exercises 257

11 χ2 Test 261

11.1 Contingency Table 262

11.2 χ2Test for a 2 × 2 Contingency Table 266

11.3 χ2 Test for R × C Contingency Tables 276

11.4 χ2 Goodness-of-Fit Test 280

11.5 Summary 284

11.6 Exercises 285

12 Nonparametric Tests Based on Rank 289

12.1 Concept of Order Statistics 289

12.2 Wilcoxon’s Signed-Rank Test for Paired Samples 290

12.3 Wilcoxon’s Rank-Sum Test for Two Independent Samples 295

12.4 Kruskal-Wallis Test for Multiple Independent Samples 299

12.5 Friedman’s Test for Randomized Block Design 303

12.6 Further Considerations About Nonparametric Tests 306

12.7 Summary 306

12.8 Exercises 306

13 Simple Linear Regression 311

13.1 Concept of Simple Linear Regression 311

13.2 Establishment of Regression Model 314

13.3 Application of Regression Model 321

13.4 Evaluation of Model Fitting 325

13.5 Summary 327

13.6 Exercises 328

14 Simple Linear Correlation 331

14.1 Concept of Simple Linear Correlation 331

14.2 Hypothesis Testing of Correlation Coefficient 336

14.3 Confidence Interval Estimation for Correlation Coefficient 338

14.4 Spearman’s Rank Correlation 340

14.5 Summary 342

14.6 Exercises 343

15 Multiple Linear Regression 345

15.1 Multiple Linear Regression Model 346

15.2 Hypothesis Testing 352

15.3 Evaluation of Model Fitting 356

15.4 Other Aspects of Regression 359

15.5 Summary 364

15.6 Exercises 364

16 Logistic Regression 369

16.1 Logistic Regression Model 370

16.2 Conditional Logistic Regression Model 388

16.3 Additional Remarks 394

16.4 Summary 395

16.5 Exercises 396

17 Survival Analysis 399

17.1 Overview 400

17.2 Description of the Survival Process 405

17.3 Comparison of Survival Processes 410

17.4 Cox’s Proportional Hazards Model 414

17.5 Other Aspects of Cox’s Proportional Hazard Model 421

17.6 Summary 422

17.7 Exercises 423

18 Evaluation of Diagnostic Tests 431

18.1 Basic Characteristics of Diagnostic Tests 431

18.2 Agreement Between Diagnostic Tests 443

18.3 Receiver Operating Characteristic Curve Analysis 448

18.4 Summary 456

18.5 Exercises 457

19 Observational Study Design 461

19.1 Cross-Sectional Studies 462

19.2 Cohort Studies 469

19.3 Case-Control Studies 472

19.4 Summary 474

19.5 Exercises 475

20 Experimental Study Design 477

20.1 Overview 478

20.2 Completely Randomized Design 483

20.3 Randomized Block Design 486

20.4 Factorial Design 489

20.5 Crossover Design 491

20.6 Summary 493

20.7 Exercises 493

Appendix 495

References 549

Index 557

Subject Areas: Mathematics [PB]

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