Biostatistics: E books
Basic and Clinical Biostatistics by
Introduction to medical research -- Study designs in medical research -- Summarizing data & presenting data in tables & graphs -- Probability & related topics for making inferences about data -- Research questions about one group -- Research questions about two separate or independent groups -- Research questions about means in three or more groups -- Research questions about relationships among variables -- Analyzing research questions about survival -- Statistical methods for multiple variables -- Survey research -- Methods of evidence-based medicine and decision analysis -- Reading the medical literature.
ISBN: 9781260455373Publication Date: 2020
An Introduction to Categorical Data Analysis by
A valuable new edition of a standard reference The use of statistical methods for categorical data has increased dramatically, particularly for applications in the biomedical and social sciences. An Introduction to Categorical Data Analysis, Third Edition summarizes these methods and shows readers how to use them using software. Readers will find a unified generalized linear models approach that connects logistic regression and loglinear models for discrete data with normal regression for continuous data. Adding to the value in the new edition is: * Illustrations of the use of R software to perform all the analyses in the book * A new chapter on alternative methods for categorical data, including smoothing and regularization methods (such as the lasso), classification methods such as linear discriminant analysis and classification trees, and cluster analysis * New sections in many chapters introducing the Bayesian approach for the methods of that chapter * More than 70 analyses of data sets to illustrate application of the methods, and about 200 exercises, many containing other data sets * An appendix showing how to use SAS, Stata, and SPSS, and an appendix with short solutions to most odd-numbered exercises Written in an applied, nontechnical style, this book illustrates the methods using a wide variety of real data, including medical clinical trials, environmental questions, drug use by teenagers, horseshoe crab mating, basketball shooting, correlates of happiness, and much more. An Introduction to Categorical Data Analysis, Third Edition is an invaluable tool for statisticians and biostatisticians as well as methodologists in the social and behavioral sciences, medicine and public health, marketing, education, and the biological and agricultural sciences.
ISBN: 9781119405276Publication Date: 2018
Intuitive Biostatistics by
Intuitive Biostatistics takes a non-technical, non-quantitative approach to statistics and emphasizes interpretation of statistical results rather than the computational strategies for generating statistical data. This makes the text especially useful for those in health-science fields whohave not taken a biostatistics course before. The text is also an excellent resource for professionals in labs, acting as a conceptually oriented and accessible biostatistics guide. With an engaging and conversational tone, Intuitive Biostatistics provides a clear introduction to statistics forundergraduate and graduate students and also serves as a statistics refresher for working scientists.
ISBN: 9780190643560Publication Date: 2017
Using R for Biostatistics by
1 Introduction: Biostatistics and R -- 1.1 Purpose of this Text -- 1.2 Development of Biostatistics -- 1.3 Development of R -- 1.4 How R is Used in this Text -- 1.5 Import Data into R -- 1.6 Addendum1: Efficient Programming with R, Project Workflow, and Good Programming Practices (gpp) -- 1.7 Addendum2: Preview of Descriptive Statistics and Graphics Using R -- 1.8 Addendum3: R and Beautiful Graphics -- 1.9 Addendum4: Research Designs Used in Biostatistics -- 1.10 Prepare to Exit, Save, and Later Retrieve this R Session -- 1.11 External Data and/or Data Resources Used in this Lesson -- 2 Data Exploration, Descriptive Statistics, and Measures of Central Tendency -- 2.1 Background -- 2.2 Import Data in Comma-Separated Values (.csv) File Format and/or Self Generate the Data Using R-Based Functions -- 2.3 Organize the Data and Display the Code Book -- 2.4 Conduct a Visual Data Check Using Graphics (e.g., Figures) -- 2.5 Descriptive Statistics for Initial Analysis of the Data -- 2.6 Quality Assurance, Data Distribution, and Tests for Normality -- 2.7 Statistical Test(s) -- 2.8 Summary -- 2.9 Addendum1: Specialized External Packages and Functions -- 2.10 Addendum2: Parametric v Nonparametric -- 2.11 Addendum3: Additional Practice Datasets for Data with Normal Distribution Patterns and Data That Do Not Exhibit Normal Distribution Patterns -- 2.12 Prepare to Exit, Save, and Later Retrieve this R Session -- 2.13 External Data and/or Data Resources Used in this Lesson -- 3 Student's t-Test for Independent Samples -- 3.1 Background -- 3.2 Import Data in Comma-Separated Values (.csv) File Format and/or Self Generate the Data Using R-Based Functions -- 3.3 Organize the Data and Display the Code Book -- 3.4 Conduct a Visual Data Check Using Graphics (e.g., Figures) -- 3.5 Descriptive Statistics for Initial Analysis of the Data -- 3.6 Quality Assurance, Data Distribution, and Tests for Normality -- 3.7 Statistical Test(s) -- 3.8 Summary of Outcomes -- 3.9 Addendum1: t-Statistic v z-Statistic -- 3.10 Addendum2: Parametric v Nonparametric -- 3.11 Addendum3: Additional Practice Datasets for Data with Normal Distribution Patterns and Data That Do Not Exhibit Normal Distribution Patterns -- 3.12 Prepare to Exit, Save, and Later Retrieve This R Session -- 3.13 External Data and/or Data Resources Used in this Lesson -- 4 Student's t-Test for Matched Pairs -- 4.1 Background -- 4.2 Import Data in Comma-Separated Values (.csv) File Format and/or Self Generate the Data Using R-Based Functions -- 4.3 Organize the Data and Display the Code Book -- 4.4 Conduct a Visual Data Check Using Graphics(e.g., Figures) -- 4.5 Descriptive Statistics for Initial Analysis of the Data -- 4.6 Quality Assurance, Data Distribution, and Tests for Normality -- 4.7 Statistical Test(s) -- 4.8 Summary of Outcomes -- 4.9 Addendum1: R-Based Tools for Unstacked (e.g. Wide) Data -- 4.10 Addendum2: Stacked Data and Student's t-Test for Matched Pairs -- 4.11 Addendum 3: The Impact of N on Student's t-Test -- 4.12 Addendum 4: Parametric v Nonparametric -- 4.13 Addendum5: Additional Practice Datasets for Data with Normal Distribution Patterns and Data That Do Not Exhibit Normal Distribution Patterns -- 4.14 Prepare to Exit, Save, and Later Retrieve This R Session -- 4.15 External Data and/or Data Resources Used in this Lesson -- 5 Oneway Analysis of Variance (ANOVA) -- 5.1 Background -- 5.2 Import Data in Comma-Separated Values (.csv) File Format and/or Self Generate the Data Using R-Based Functions -- 5.3 Organize the Data and Display the Code Book -- 5.4 Conduct a Visual Data Check Using Graphics(e.g., Figures) -- 5.5 Descriptive Statistics for Initial Analysis of the Data -- 5.6 Quality Assurance, Data Distribution, and Tests for Normality -- 5.7 Statistical Test(s) -- 5.8 Summary of Outcomes -- 5.9 Addendum1: Other Packages for Display of Oneway ANOVA -- 5.10 Addendum2: Parametric v Nonparametric -- 5.11 Addendum3: Additional Practice Data Sets -- 5.12 Prepare to Exit, Save, and Later Retrieve This R Session -- 5.13 External Data and/or Data Resources Used in this Lesson -- 6 Twoway Analysis of Variance (ANOVA) -- 6.1 Background -- 6.2 Import Data in Comma-Separated Values (.csv) File Format and/or Self Generate the Data Using R-Based Functions -- 6.3 Organize the Data and Display the Code Book -- 6.4 Conduct a Visual Data Check Using Graphics (e.g., Figures) -- 6.5 Descriptive Statistics for Initial Analysis of the Data -- 6.6 Quality Assurance, Data Distribution, and Tests for Normality -- 6.7 Statistical Test(s) -- 6.8 Summary of Outcomes -- 6.9 Addendum 1: Other Packages for Display of Twoway ANOVA -- 6.10 Addendum 2: Parametric v Nonparametric -- 6.11 Addendum 3: Additional Practice Data Sets -- 6.12 Prepare to Exit, Save, and Later Retrieve This R Session -- 6.13 External Data and/or Data Resources Used in this Lesson -- 7 Correlation, Association, Regression, Likelihood, and Prediction -- 7.1 Background -- 7.2 Import Data in Comma-Separated Values (.csv) File Format and/or Self Generate the Data Using R-Based Functions -- 7.3 Organize the Data and Display the Code Book -- 7.4 Quality Assurance, Data Distribution, and Tests for Normality -- 7.5 Statistical Test(s) -- 7.6 Summary of Outcomes -- 7.7 Addendum 1: Multiple Regression -- 7.8 Addendum 2: Likelihood and Odds Ratio -- 7.9 Addendum 3:Parametric v Nonparametric -- 7.10 Addendum 4: Additional Practice Data Sets -- 7.11 Prepare to Exit, Save, and Later Retrieve This R Session -- 7.12 External Data and/or Data Resources Used in this Lesson -- 8 Working with Large and Complex Datasets -- 8.1 Background -- 8.2 Import Data in Comma-Separated Values (.csv) File Format and/or Self Generate the Data Using R-Based Functions -- 8.3 Organize the Data and Display the Code Book -- 8.4 Conduct a Visual Data Check Using Graphics (e.g., Figures) -- 8.5 Descriptive Statistics for Initial Analysis of the Data -- 8.6 Quality Assurance, Data Distribution, and Tests for Normality -- 8.7 Statistical Test(s) -- 8.8 Summary of Outcomes -- 8.9 Addendum1: Additional Graphics, to Show Relationships Between and Among Data -- 8.10 Addendum2: Graphics Using the lattice Package -- 8.11 Addendum3: Graphics Using the ggplot2 Package -- 8.12 Addendum 4: Beyond an Introduction to R - Use the tidyverse to Create Subsets of Original Datasets -- 8.13 Prepare to Exit, Save, and Later Retrieve This R Session -- 8.14 External Data and/or Data Resources Used in this Lesson -- 9 Future Actions and Next Steps -- 9.1 Use of This Text -- 9.2 R and Beautiful Reporting with R Markdown -- 9.3 Future Use of R for Biostatistics -- 9.4 Big Data and Bio Informatics -- 9.5 External Resources -- 9.6 Contact the Authors. .
ISBN: 9783030624040Publication Date: 2021
Epidemiology and Biostatistics An Introduction to Clinical Research by ISBN: 9783319966441Publication Date: 2019
