Full Description
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.
Contents
1 Why R is essential? What are the prospects by learning R?.- 2 An overview of statistical analysis plan for clinical studies.- 3 Introduction to R environment and basic commands.- 4 Data handling and manipulation in R with Descriptive Statistics.- 5 Introduction to packages in R - installation, loading, unloading and deletion.- 6 Visualisation of data - basic and advanced.- 7 Inferential statistics for the hypothesis testing of parametrically distributed data.- 8 Inferential statistics for the hypothesis testing of non-parametric data.- 9 Computation of sample size for clinical studies.- 10 Correlation and linear regression analysis for continuous outcome.- 11 Logistic regression analysis for categorical outcome.- 12 Receiver Operating Characteristic (ROC) curve analysis for diagnostic studies.- 13 Survival analysis for time to event-based outcome.- 14 Conducting randomization in clinical trials.- 15 Development of web-based interactive servers using R shiny package.
