Statisticians and statistical programmers spend a great deal of time analyzing data and producing reports for clinical trials, both for final trial reports and for interim reports for data monitoring committees. Point and Click interfaces and copy-and-paste are now believed to be bad models for reproducible research. Instead, there are advantages to developing a high-level language for producing common elements of reports related to accrual, exclusions, descriptive statistics, adverse events, time to event, and longitudinal data.
It is well appreciated in the statistical and graphics design communities that graphics are much better than tables for conveying numeric information. There are thus advantages for having statistical reports for clinical trials that are almost completely graphical. Instead of devoting space to tables,
In this talk I will describe R packages
greport (using a
LaTeX pdf model) and
hreport (using an html model).
Rmarkdown are used to compose the reproducible reports.
hreport compose all figure and table captions. They contain high-level abstractions of common clinical trial reporting tasks to minimize programming by the use. Before showing examples of these report-making packages, I’ll show some of the new graphical building blocks in the
rms packages. These new functions make use of the
plotly package to create interactive graphics using