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

ISBN-13: 9781119722595 / Angielski / Twarda / 2021 / 640 str.

Robert P. Hirsch

Recenzje

"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

Spis treści

PrefacePART ONE: Basic Concepts1.Thinking About Chance1.1.Properties of Chance1.2.Combinations of events1.2.1 Intersections1.2.2 Unions1.3.Bayes' theorem2.Describing Populations2.1.Types of data2.2.Describing distributions graphically2.2.1. Graphing discrete data2.2.2. Graphing continuous data2.2.3. Frequency polygon2.3.Describing distributions mathematically2.3.1. Parameter of location2.3.2. Parameter of dispersion2.4 Taking chance into account2.4.1 Standard normal distribution3.Examining Samples3.1.Nature of samples3.2.Estimation3.2.1 Point estimates3.2.2 The sampling distribution3.2.3 Interval estimates3.3.Hypothesis testingPART TWO: Univariable Analysis4.Univariable Analysis of a Continuous Dependent Variable4.1.Student's t distribution4.2.Interval estimation4.3.Hypothesis testing5.Univariable Analysis of an Ordinal Dependent Variable5.1 Nonparametric methods5.2 Estimation5.3 Wilcoxon signed-rank test5.4 Statistical power of nonparametric tests6.Univariable Analysis of a Nominal Dependent Variable6.1.Distributions of nominal data6.2.Point estimates6.2.1 Proportions6.2.2 Rates6.3.Sampling distributions6.3.1 Binomial distribution6.3.2 Poisson distribution6.4.Interval estimation6.5.Hypothesis testingPART THREE: Bivariable Analysis7.Bivariable Analysis of a Continuous Dependent Variable7.1.Continuous independent variable7.1.1 Regression analysis7.1.2 Correlation analysis7.2.Ordinal independent variable7.3.Nominal independent variable7.3.1 Estimating the difference between groups7.3.2 Taking chance into account8.Bivariable Analysis of an Ordinal Dependent Variable8.1.Ordinal independent variable8.2.Nominal independent variable9.Bivariable Analysis of a Nominal Dependent Variable9.1.Continuous independent variable9.1.1 Estimation9.1.2 Hypothesis testing9.2.Nominal independent variable9.2.1 Dependent variable not affected by time: Unpaired design9.2.2 Dependent variable not affected by time: Paired design9.2.3 Dependent variable affected by timePART FOUR: Multivariable Analysis10.Multivariable Analysis of a Continuous Dependent Variable10.1.Continuous independent variables10.1.1 Multiple regression analysis10.1.2 Multiple correlation analysis10.2.Nominal independent variables10.2.1 Analysis of variance10.2.2 Posterior tests10.3.Continuous and nominal independent variables10.3.1 Indicator ("dummy") variables10.3.2 Interaction variables10.3.3 General linear model11.Multivariable Analysis of an Ordinal Dependent Variable11.1.Nonparametric ANOVA11.2.Posterior testing12.Multivariable Analysis of a Nominal Dependent Variable12.1.Continuous and/or nominal independent variables12.1.1 Maximum likelihood estimation12.1.2 Logistic regression analysis12.1.3 Cox regression analysis12.2.Nominal independent variables12.2.1 Stratified analysis12.2.2 Life table analysis13. Testing Assumptions13.1Continuous dependent variables13.1.1 Assuming a Gaussian distribution13.1.2 Transforming dependent variables13.1.3 Assuming equal variances13.1.4 Assuming additive relationships13.2Nominal dependent variables13.2.1 Assuming a Gaussian distribution13.2.2 Assuming equal variances13.2.3 Assuming additive relationships13.3Independent variables

O autorze

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.