Introduction to Biostatistical Applications in Health Research with Microsoft Office Excel

Robert P. Hirsch, PhD, is on the faculty for the Foundation for the Advanced Education in the Sciences within the Graduate School at the National Institutes of Health.  He is also a retired Professor of Epidemiology and Biostatistics and Adjunct Professor of Statistics at The George Washington University.  Dr. Hirsch is the author of numerous books in the field of health research and practice.

• Preface ixAcknowledgements xiNotices xiiiAbout The Companion Website xvPart One Basic Concepts 11 Thinking about Chance 31.1 Properties of Probability 31.2 Combinations of Events 71.2.1 Intersections 81.2.2 Unions 131.3 Bayes’ Theorem 152 Describing Distributions 182.1 Types of Data 192.2 Describing Distributions Graphically 192.2.1 Graphing Discrete Data 202.2.2 Graphing Continuous Data 222.3 Describing Distributions Mathematically 262.3.1 Parameter of Location 272.3.2 Parameter of Dispersion 312.4 Taking Chance into Account 382.4.1 Standard Normal Distribution 393 Examining Samples 493.1 Nature of Samples 503.2 Estimation 513.2.1 Point Estimates 513.2.2 The Sampling Distribution 563.2.3 Interval Estimates 603.3 Hypothesis Testing 643.3.1 Relationship between Interval Estimation and Hypothesis Testing 72Part Two Univariable Analyses 754 Univariable Analysis of a Continuous Dependent Variable 794.1 Student’s t-Distribution 814.2 Interval Estimation 844.3 Hypothesis Testing 865 Univariable Analysis of an Ordinal Dependent Variable 905.1 Nonparametric Methods 905.2 Estimation 945.3 Wilcoxon Signed-Rank Test 955.4 Statistical Power of Nonparametric Tests 976 Univariable Analysis of a Nominal Dependent Variable 996.1 Distribution of Nominal Data 1006.2 Point Estimates 1016.2.1 Proportions 1016.2.2 Rates 1046.3 Sampling Distributions 1086.3.1 Binomial Distribution 1086.3.2 Poisson Distribution 1126.4 Interval Estimation 1146.5 Hypothesis Testing 117Part Three Bivariable Analyses 1217 Bivariable Analysis of a Continuous Dependent Variable 1237.1 Continuous Independent Variable 1237.1.1 Regression Analysis 1257.1.2 Correlation Analysis 1497.2 Ordinal Independent Variable 1657.3 Nominal Independent Variable 1667.3.1 Estimating the Difference between the Groups 1667.3.2 Taking Chance into Account 1678 Bivariable Analysis of an Ordinal Dependent Variable 1758.1 Ordinal Independent Variable 1768.2 Nominal Independent Variable 1849 Bivariable Analysis of a Nominal Dependent Variable 1899.1 Continuous Independent Variable 1909.1.1 Estimation 1919.1.2 Hypothesis Testing 1989.2 Nominal Independent Variable 2009.2.1 Dependent Variable Not Affected by Time: Unpaired Design 2019.2.2 Hypothesis Testing 2089.2.3 Dependent Variable Not Affected by Time: Paired Design 2189.2.4 Dependent Variable Affected by Time 223Part Four Multivariable Analyses 22710 Multivariable Analysis of a Continuous Dependent Variable 22910.1 Continuous Independent Variables 23010.1.1 Multiple Regression Analysis 23110.1.2 Multiple Correlation Analysis 24710.2 Nominal Independent Variables 24810.2.1 Analysis of Variance 24910.2.2 Posterior Testing 25810.3 Both Continuous and Nominal Independent Variables 26510.3.1 Indicator (Dummy) Variables 26610.3.2 Interaction Variables 26710.3.3 General Linear Model 27311 Multivariable Analysis of an Ordinal Dependent Variable 28111.1 Nonparametric Analysis of Variance 28211.2 Posterior Testing 28812 Multivariable Analysis of a Nominal Dependent Variable 29312.1 Continuous And/or Nominal Independent Variables 29412.1.1 Maximum Likelihood Estimation 29412.1.2 Logistic Regression Analysis 29712.1.3 Cox Regression Analysis 30612.2 Nominal Independent Variables 30712.2.1 Stratified Analysis 30812.2.2 Relationship between Stratified Analysis and Logistic Regression 31812.2.3 Life Table Analysis 322Appendix A: Flowcharts 335Appendix B: Statistical Tables 341Appendix C: Standard Distributions 377Appendix D: Excel Primer 380Index 385