Biostatistics for Clinical Research
Theory and Applications in R
This version has been written in Scrivener, exported as plain text as a single index.Rmd file and then used to build the book. If this worked, then it’s an entirely new workflow for writing and self publishing books that takes advantage of the organizing and writing features of Scrivener, and the computational analysis and publishing features of R, Rmarkdown and Bookdown.
At this point I’ve created a special compile format in Scrivener that puts an empty line between each folder and file. Each folder functions as a “Part” of the book, and each text file (in Scrivener) as a chapter in that part. However, Scrivener compiles one Index file which may make editorial contributions more challenging since the entire book is in one large file on Github and in bookdown. But that is something I’m willing to live with since the benefits of organizing, creating and writing in Scrivener for the project are well worthwhile.
Biostatistics for Clinical Research: Theory and Applications in R is a book for the Biostatistics sequence of courses (1 & 2) at the University of Jamestown PhD in Clinical Research program. The book teaches and utilizes examples with the open source computational platform R ( https://cran.r-project.org/). There is an emphasis and equal distribution of theory and application through all chapters. There is an underlying emphasis on performing all applications utilizing reproducible research approaches (programming with sufficient commentary) and tools (GitHub, Figshare, Open Science Framework, etc) throughout the book.
The book covers the theoretical foundations of biostatistics and the associated computational approaches of exploratory, descriptive and inferential biostatistical methods for the analysis of data in clinical research (observational and experimental). These methods are based on probability theory and include assessing the impact of chance and variability on the interpretation of research findings. Topics include probability theory, measurement theory, descriptive and exploratory analysis, analysis of assumptions and visualization; hypothesis testing; methods of comparison of discrete and continuous data including t-test, correlation, regression, and general linear models (including ANOVA). Applications and examples utilize the R statistical programming language to apply theoretical topics with real world data.