Details

Introduction to Biostatistical Applications in Health Research with Microsoft Office Excel and R

2. Aufl.

von: Robert P. Hirsch
114,99 €
Verlag:	Wiley
Format:	EPUB
Veröffentl.:	28.01.2021
ISBN/EAN:	9781119722649
Sprache:	englisch
Anzahl Seiten:	640

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Beschreibungen

Titelbeschreibung

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

Inhaltsverzeichnis

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

Rezension

"This book provides a good introduction to biostatistics with a lot of medical examples and exercises. It is perfect for those that have basic notions on mathematics, explaining the main formulas necessary for describing, testing and finding out the relationships between data ... the manuscript is very good, comprehensive in information, the chapters are well structured, it includes a great arsenal of examples analysed and described in the field of biostatistics at a basic level. The reader should achieve a solid first step knowledge in the area, both for the statistical concepts and also practical applications." – International Society for Clinical Biostatistics News

Autorenportrait

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.

Back cover copy

Learn more about the foundations of statistical methods in health research with this authoritative new resource 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: <ul> <li>Extensive discussions of basic and foundational concepts in statistical methods, including Bayes??? Theorem, populations, and samples</li> <li>A treatment of univariable analysis, covering topics like continuous dependent variables and ordinal dependent variables</li> <li>An examination of bivariable analysis, including regression analysis and correlation analysis</li> <li>An analysis of multivariate calculations in statistics and how testing assumptions, like assuming Gaussian distributions or equal variances, affect statistical outcomes</li> </ul> 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.