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

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

Robert P. Hirsch

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640 pages, parution le 26/04/2021
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Résumé

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 PART ONE: Basic Concepts 1.Thinking About Chance 1.1.Properties of Chance 1.2.Combinations of events 1.2.1 Intersections 1.2.2 Unions 1.3.Bayes' theorem 2.Describing Populations 2.1.Types of data 2.2.Describing distributions graphically 2.2.1. Graphing discrete data 2.2.2. Graphing continuous data 2.2.3. Frequency polygon 2.3.Describing distributions mathematically 2.3.1. Parameter of location 2.3.2. Parameter of dispersion 2.4 Taking chance into account 2.4.1 Standard normal distribution 3.Examining Samples 3.1.Nature of samples 3.2.Estimation 3.2.1 Point estimates 3.2.2 The sampling distribution 3.2.3 Interval estimates 3.3.Hypothesis testing PART TWO: Univariable Analysis 4.Univariable Analysis of a Continuous Dependent Variable 4.1.Student's t distribution 4.2.Interval estimation 4.3.Hypothesis testing 5.Univariable Analysis of an Ordinal Dependent Variable 5.1 Nonparametric methods 5.2 Estimation 5.3 Wilcoxon signed-rank test 5.4 Statistical power of nonparametric tests 6.Univariable Analysis of a Nominal Dependent Variable 6.1.Distributions of nominal data 6.2.Point estimates 6.2.1 Proportions 6.2.2 Rates 6.3.Sampling distributions 6.3.1 Binomial distribution 6.3.2 Poisson distribution 6.4.Interval estimation 6.5.Hypothesis testing PART THREE: Bivariable Analysis 7.Bivariable Analysis of a Continuous Dependent Variable 7.1.Continuous independent variable 7.1.1 Regression analysis 7.1.2 Correlation analysis 7.2.Ordinal independent variable 7.3.Nominal independent variable 7.3.1 Estimating the difference between groups 7.3.2 Taking chance into account 8.Bivariable Analysis of an Ordinal Dependent Variable 8.1.Ordinal independent variable 8.2.Nominal independent variable 9.Bivariable Analysis of a Nominal Dependent Variable 9.1.Continuous independent variable 9.1.1 Estimation 9.1.2 Hypothesis testing 9.2.Nominal independent variable 9.2.1 Dependent variable not affected by time: Unpaired design 9.2.2 Dependent variable not affected by time: Paired design 9.2.3 Dependent variable affected by time PART FOUR: Multivariable Analysis 10.Multivariable Analysis of a Continuous Dependent Variable 10.1.Continuous independent variables 10.1.1 Multiple regression analysis 10.1.2 Multiple correlation analysis 10.2.Nominal independent variables 10.2.1 Analysis of variance 10.2.2 Posterior tests 10.3.Continuous and nominal independent variables 10.3.1 Indicator ("dummy") variables 10.3.2 Interaction variables 10.3.3 General linear model 11.Multivariable Analysis of an Ordinal Dependent Variable 11.1.Nonparametric ANOVA 11.2.Posterior testing 12.Multivariable Analysis of a Nominal Dependent Variable 12.1.Continuous and/or nominal independent variables 12.1.1 Maximum likelihood estimation 12.1.2 Logistic regression analysis 12.1.3 Cox regression analysis 12.2.Nominal independent variables 12.2.1 Stratified analysis 12.2.2 Life table analysis 13. Testing Assumptions 13.1Continuous dependent variables 13.1.1 Assuming a Gaussian distribution 13.1.2 Transforming dependent variables 13.1.3 Assuming equal variances 13.1.4 Assuming additive relationships 13.2Nominal dependent variables 13.2.1 Assuming a Gaussian distribution 13.2.2 Assuming equal variances 13.2.3 Assuming additive relationships 13.3Independent variablesROBERT 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.

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Éditeur(s) Wiley
Auteur(s) Robert P. Hirsch
Parution 26/04/2021
Nb. de pages 640
EAN13 9781119722595

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