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R for Clinical Trial Reporting


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, HTML5 and Javascript in R html reports makes it easy to show tabular information in pop-up text when hovering the mouse over a graphical element.

In this talk I will describe R packages greport (using a LaTeX pdf model) and hreport (using an html model). knitr and Rmarkdown are used to compose the reproducible reports. greport and 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 Hmisc and rms packages. These new functions make use of the plotly package to create interactive graphics using Javascript and D3.

2019-11-08 00:00
Frank Harrell
Frank Harrell
Professor of Biostatistics

My research interests include Bayesian statistics, predictive modeling and model validation, statistical computing and graphics, biomedical research, clinical trials, health services research, cardiology, and COVID-19 therapeutics.

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