Basic and Clinical Biostatistics by Susan E. White 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.

Epidemiology and Biostatistics An Introduction to Clinical Research by B. Kestenbaum This is a concise introduction to epidemiology and biostatistics written specifically for medical students and first-time learners of clinical research methods. It presents the core concepts of epidemiology and of biostatistics and illustrates them with extensive examples from the clinical literature. It is the only book on the market written to speak directly to medical students and first-time biomedical researchers by using language and examples that are easy to understand. This newly updated second edition is extensively rewritten to provide the clearest explanations and examples. There is also a sister-text, a 150-problem workbook of practice problems that can be purchased alongside this textbook. The author continues to provide a text that is attractively fast-paced and concise for use in condensed courses, such as those taught in medical school. The book is an excellent review for the epidemiology section of the United States Medical Licensing Examination Part I which all medical students must take at the end of the second year.

An Introduction to Categorical Data Analysis by Alan Agresti 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.

Introduction to Data Science in Biostatistics by Thomas W. MacFarland Introduction to Data Science in Biostatistics: Using R, the Tidyverse Ecosystem, and APIs defines and explores the term "data science" and discusses the many professional skills and competencies affiliated with the industry. With data science being a leading indicator of interest in STEM fields, the text also investigates this ongoing growth of demand in these spaces, with the goal of providing readers who are entering the professional world with foundational knowledge of required skills, job trends, and salary expectations. The text provides a historical overview of computing and the field's progression to R as it exists today, including the multitude of packages and functions associated with both Base R and the tidyverse ecosystem. Readers will learn how to use R to work with real data, as well as how to communicate results to external stakeholders. A distinguishing feature of this text is its emphasis on the emerging use of APIs to obtain data.

Intuitive Biostatistics by Harvey Motulsky 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.

Practical Biostatistics for Medical and Health Sciences by Seyed Hassan Saneii; Hassan Doosti This book addresses the challenge of presenting biostatistics to medical and health science audiences coherently. Tailored for students and researchers, its 13 chapters progress logically from foundational concepts like measurement scales and statistical calculations to advanced topics such as probability, correlation, regression and health and disease measures. Practical examples enhance relevance, and its gradual approach ensures easy comprehension even for non-statisticians. The book's practical emphasis shines as it culminates in teaching the use of SPSS software for result interpretation, bridging theory and practice effectively. It empowers medical professionals to confidently understand and apply statistical concepts in their work, serving as an indispensable resource in navigating the intricacies of biostatistics in medical and health sciences.

R for Basic Biostatistics in Medical Research by Anand Srinivasan; Archana Mishra; Praveen Kuman-M. The scientific community at the global level is fast becoming aware of the rising use of open-source tools such as R and Python for data analysis. Unfortunately, in spite of the awareness, the conversion of the intrigue to the practical knowledge in utilization of the open-source tools for routine day-to-day data analysis is seriously lacking both among physicians and medical scientists. This book enables physician-scientists to understand the complexity of explaining a programming/ data-analytic language to a healthcare professional and medical scientist. It simplifies and explains how R can be used in medical projects and routine office works. It also talks about the methodologies to convert the knowledge to practice. The book starts with the introduction to the structure of R programming language in the initial chapters, followed with explanations of utilizing R in the basics of data analysis like data importing and exporting, operations on a data frame, parametric and non-parametric tests, regression, sample size calculation, survival analysis, receiver operator characteristic analysis (ROC) and techniques of randomization. Each chapter provides a brief introduction to the involved statistics, for example, dataset, working codes, and a section explaining the codes. In addition to it, a chapter has been dedicated to describing the ways to generate plots using R. This book primarily targets health care professionals and medical/life-science researchers in general.

Using R for Biostatistics by MacFarland, Thomas ; Jan M Yates 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. .