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Vanderbilt Department of Biostatistics

Office of Biostatistics FDA CDER

- learn principles of graph construction
- learn which features of summary statistics should be emphasized in a graph
- see examples of increasing information using modern graphics, whether static or interactive
- understand features of html notebooks for statistical reports
- learn how to use
`RStudio`

to make html notebooks - learn to use new functions in the R
`Hmisc`

package which use the R`plotly`

package to produce somewhat interactive graphics - get ideas for constructing your own interactive graphics for statistical reports by seeing examples of placing supplemental information in initially hidden layers of graphics

- providing a more interpretable snapshot of study results
- displaying trends and finding patterns
- speeding up reviews
- displaying data that are too messy to model (e.g., sites within countries within regions)
- displaying marginal (unadjusted) estimates
- replacing tables having many numbers, especially when continuous variables are involved
- showing whole distribution of continuous variables
- finding data problems
- finding problematic data distributions including excessive ties
- displaying results of complex statistical models

- displaying finest levels of details
- providing exact numeric values for elements of graphs

- best way to handle confounding and outcome heterogeneity
- accounts for more variables than any expert could understand in a graph
- handles continuous variables optimally
- handles censoring, incomplete data, large datasets, and heavy ties in data

- Applying Human Factors Research to Statistical Graphics by John Rauser
- Principles of Graph Construction (see Sections 2.4 and 2.5 for examples related to drug safety)
- Clinical Trial Reports: R+LaTeX+pdf
- Example report 1
- Example report 2
- See also this

- R +
`RStudio`

+`Rmarkdown`

+`knitr`

+`plotly`

+ html- R
`Hmisc`

package examples - R
`rms`

package examples (includes plots of predicted values + interactive survival curves)

- R
- R
`hreport`

package: html Clinical Trial Reports

(under development; funded by NIH NCATS CTSA Vanderbilt Institute for Clinical and Translational Research) - Safety Explorer example (Rmarkdown html notebook)

Authors: Becca Krouse, Spencer Childress, Jeremy Wildfire

Rho, Inc.

- Zip file containing all files above
- Safety Explorer - this can be used in conjunction with the
`hreport`

package,`knitr`

, and`RStudio`

to produce highly interactive tables with small graphics, will drill-down capability from body systems to preferred terms within body systems. A self-contained`html`

report can be produced, and the user interacts without installing any software or using an internet connection. - Flex dashboards for monitoring clinical studies
- Presentation of clinical laboratory results: an experimental comparison of four visualization techniques
- The design and evaluation of a graphical display for laboratory data
- Graphical summary of patient status by Powsner and Tufte
- http://www.datavisualisation-r.com/
- Effective Displays of Data Need More Attention in Statistics Education by Thomas Bradstreet, Michael Nessly, and Thomas Short
- Graphics for Clinical Trials by Jonathan Levine
- Exploratory Analysis of Clinical Safety Data to Detect Safety Signals (Frank Harrell and Thomas Burgan).
- http://motioninsocial.com/tufte/
- Schwarz' Tour of Bad Graphs
- https://www.rstudio.com
- http://rmarkdown.rstudio.com
- http://rmarkdown.rstudio.com/r_notebooks.html
- http://yihui.name/knitr/
- http://biostat.mc.vanderbilt.edu/Hmisc
- http://biostat.mc.vanderbilt.edu/Rrms
- https://plot.ly/r
- https://plot.ly/r/getting-started
- ggplotly: a function that converts any ggplot2 graphic to a plotly interactive graphic: https://plot.ly/ggplot2
- Github Repositories

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Topic revision: r13 - 12 Mar 2018, FrankHarrell

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