Introduction to Biostatistical Applications in Health Research with Microsoft Office Excel and R (2^nd Ed.)

The second edition of Introduction to Biostatistical Applications in Health Research delivers a thorough examination of the basic techniques and most commonly used statistical methods in health research. Retaining much of what was popular with the well-received first edition, the thoroughly revised second edition includes a new chapter on testing assumptions and how to evaluate whether those assumptions are satisfied and what to do if they are not.

The newest edition contains brand-new code examples for using the popular computer language R to perform the statistical analyses described in the chapters within. You'll learn how to use Excel to generate datasets for R, which can then be used to conduct statistical calculations on your data.

The book also includes a companion website with a new version of BAHR add-in programs for Excel. This new version contains new programs for nonparametric analyses, Student-Newman-Keuls tests, and stratified analyses. Readers will also benefit from coverage of topics like:

• Extensive discussions of basic and foundational concepts in statistical methods, including Bayes' Theorem, populations, and samples
• A treatment of univariable analysis, covering topics like continuous dependent variables and ordinal dependent variables
• An examination of bivariable analysis, including regression analysis and correlation analysis
• An analysis of multivariate calculations in statistics and how testing assumptions, like assuming Gaussian distributions or equal variances, affect statistical outcomes

Perfect for health researchers of all kinds, Introduction to Biostatistical Applications in Health Research also belongs on the bookshelves of anyone who wishes to better understand health research literature. Even those without a great deal of mathematical background will benefit greatly from this text.

Preface to First Edition xiii

Preface to Second Edition xv

About the Companion Website xvii

Part One Basic Concepts 1

1 Thinking About Chance 3

1.1 Properties of Probability 4

1.2 Combinations of Event 8

1.2.1 Intersections 8

1.2.2 Unions 13

1.3 Bayes’ Theorem 16

Chapter Summary 19

Exercises 20

2 Describing Distributions 25

2.1 Types of Data 26

2.2 Describing Distributions Graphically 27

2.2.1 Graphing Discrete Data 27

2.2.2 Graphing Continuous Data 30

2.3 Describing Distributions Mathematically 36

2.3.1 Parameter of Location 37

2.3.2 Parameter of Dispersion 41

2.4 Taking Chance into Account 48

2.4.1 Standard Normal Distribution 49

Chapter Summary 59

Exercises 62

3 Examining Samples 65

3.1 Nature of Samples 66

3.2 Estimation 67

3.2.1 Point Estimates 67

3.2.2 The Sampling Distribution 73

3.2.3 Interval Estimates 78

3.3 Hypothesis Testing 82

3.3.1 Relationship Between Interval Estimation and Hypothesis Testing 89

Chapter Summary 91

Exercises 93

Part Two Univariable Analyses 97

4 Univariable Analysis of A Continuous Dependent Variable 101

4.1 Student’s t-Distribution 103

4.2 Interval Estimation 106

4.3 Hypothesis Testing 109

Chapter Summary 113

Exercises 114

5 Univariable Analysis of An Ordinal Dependent Variable 119

5.1 Nonparametric Methods 120

5.2 Estimation 123

5.3 Wilcoxon Signed-Rank Test 124

5.4 Statistical Power of Nonparametric Tests 128

Chapter Summary 128

Exercises 129

6 Univariable Analysis of A Nominal Dependent Variable 133

6.1 Distribution of Nominal Data 134

6.2 Point Estimates 135

6.2.1 Probabilities 136

6.2.2 Rates 138

6.3 Sampling Distributions 142

6.3.1 Binomial Distribution 143

6.3.2 Poisson Distribution 146

6.4 Interval Estimation 149

6.5 Hypothesis Testing 151

Chapter Summary 155

Exercises 156

Part Three Bivariable Analyses 161

7 Bivariable Analysis of A Continuous Dependent Variable 163

7.1 Continuous Independent Variable 163

7.1.1 Regression Analysis 165

7.1.2 Correlation Analysis 189

7.2 Ordinal Independent Variable 207

7.3 Nominal Independent Variable 207

7.3.1 Estimating the Difference between the Groups 208

7.3.2 Taking Chance into Account 209

Chapter Summary 218

Exercises 221

8 Bivariable Analysis of An Ordinal Dependent Variable 227

8.1 Ordinal Independent Variable 228

8.2 Nominal Independent Variable 236

Chapter Summary 241

Exercises 243

9 Bivariable Analysis of A Nominal Dependent Variable 245

9.1 Continuous Independent Variable 246

9.1.1 Estimation 247

9.1.2 Hypothesis Testing 255

9.2 Nominal Independent Variable 258

9.2.1 Dependent Variable Not Affected by Time: Unpaired Design 259

9.2.2 Hypothesis Testing 266

9.2.3 Dependent Variable Not Affected by Time: Paired Design 277

9.2.4 Dependent Variable Affected by Time 283

Chapter Summary 286

Exercises 288

Part Four Multivariable Analyses 293

10 Multivariable Analysis of A Continuous Dependent Variable 295

10.1 Continuous Independent Variables 296

10.1.1 Multiple Regression Analysis 297

10.1.2 Multiple Correlation Analysis 317

10.2 Nominal Independent Variables 319

10.2.1 Analysis of Variance 320

10.2.2 Posterior Testing 331

10.3 Both Continuous and Nominal Independent Variables 340

10.3.1 Indicator (Dummy) Variables 341

10.3.2 Interaction Variables 343

10.3.3 General Linear Model 348

Chapter Summary 355

Exercises 358

11 Multivariable Analysis of An Ordinal Dependent Variable 367

11.1 Nonparametric Anova 369

11.2 Posterior Testing 375

Chapter Summary 380

Exercises 381

12 Multivariable Analysis of A Nominal Dependent Variable 385

12.1 Continuous and/or Nominal Independent Variables 387

12.1.1 Maximum Likelihood Estimation 387

12.1.2 Logistic Regression Analysis 389

12.1.3 Cox Regression Analysis 399

12.2 Nominal Independent Variables 401

12.2.1 Stratified Analysis 402

12.2.2 Relationship Between Stratified Analysis and Logistic Regression 410

12.2.3 Life Table Analysis 414

Chapter Summary 424

Exercises 427

13 Testing Assumptions 433

13.1 Continuous Dependent Variables 436

13.1.1 Assuming A Gaussian Distribution 437

13.1.2 Transforming Dependent Variables 477

13.1.3 Assuming Equal Variances 485

13.1.4 Assuming Additive Relationships 494

13.1.5 Dealing With Outliers 506

13.2 Nominal Dependent Variables 507

13.2.1 Assuming a Gaussian Distribution 507

13.2.2 Assuming Equal Variances 510

13.2.3 Assuming Additive Relationships 511

13.3 Independent Variables 511

Chapter Summary 513

Exercises 516

Appendix A: Flowcharts 521

Appendix B: Statistical Tables 527

Appendix C: Standard Distributions 597

Appendix D: Excel Primer 601

Appendix E: R Primer 605

Appendix F: Answers To Odd Exercises 609

Index 611

ROBERT P. HIRSCH, PHD, is on the faculty at the Foundation for Advanced Education in the Sciences as well as a Medical Research Consultant with over thirty years of experience. He received his doctorate in Biology at Kansas State University. He was formerly Professor at the George Washington University - Columbian College of Arts & Science where he helped to develop the Epidemiology and Biostatistics Programs.