Creation of a clinical data review report

Laure Cougnaud, Michela Pasetto

October 09, 2022

Utility functions are provided to create a standard clinical data review report. Please note that the examples in this section are only informative, and not evaluated in the vignette itself.


1 Create an example report

To create your clinical data review report, we advise to start from a skeleton of such report, via:


This report is based on an example dataset (subset of the CDISC Pilot 01 dataset), available in the clinUtils package.

The report is created via:


You can further tailor the report to your dataset(s) of interest and sections of interest.

See the next sections for further informations.

2 File structure

2.1 Input

The input for the clinical data report consists of a set of Rmardown files and a config directory.

Rmd files

A set of Rmarkdown files in the working directory should be

Config files

There should be a config directory containing:

Please see the section Template reports for details on the template reports.

2.2 Output

The clinical data report consists of a final report output and a set of intermediary results.

Final output

A final output folder (‘report’ by default) containing the:

Intermediary results

A folder of intermediary results (‘interim’ by default) containing:

3 Template reports

A set of template reports are available in the clinDataReview package itself.

The full list of the reports and the corresponding input parameters is available at:
? `clinDataReview-templates` (please note the backticks!).

To use a template available in the package for one of your report, your config file should contain the corresponding name of the template via the template tag and the templatePackage set to ‘clinDataReview’.

For example, to include a division in the report, your YAML config file should be formatted as:

template: divisionTemplate.Rmd
templatePackage: clinDataReview
reportTitle: "Title for a chapter"
reportTitleLevel: 1

Please note that you can create your own template reports and store it in your custom R package (in a ‘inst/template’ folder).

4 Render a clinical data review report

4.1 Production

4.1.1 Create a report for production

The function render_clinDataReviewReport renders a clinical data report for production.

The render_clinDataReviewReport creates several html files, one per configuration file, split based on the split_by parameter.

To open the full report, the user should open the introduction.html file.

In case a lot of different html chapters are created, a landing page can be created to guide the end-user to the opening of the report.

4.1.2 Create redirect page

A convenient way to store all html files into one location, and have only one page to look for to open the report is the function:

This function:

  • stores all html files into a folder
  • create a landing page next to the folder that allows the user to navigate through the report.

The default names of the folder and landing page are report_dependencies and report.html. The names can be changed by the user.

4.1.3 Zip and send the report to collaborators

A dedicated functionality is available to zip the report and send it to colleagues within the team.

Once a report has been rendered, the user can call:

This function zips the report, so that the analyses can be easily put as attachement in a mail/uploaded to a shared drive.

If the folder is not unzipped before opening the reports, a message in the browser reminds to unzip the documents.

4.1.4 Add metadata

Metadata can be added in the landing page, containing information concerning the data set creation time, the path to the original data etc.

A metadata file should be in yaml format.

The metadata function can be directly called in a Rmd documents with

4.2 Development

To facilitate the creation of the report, a few utility functions & dedicated parameters are available.

4.2.1 Modular framework (Re-)run part(s) of the report

In case the creation of the entire report is time-consuming, and only part(s) of the report have been updated, it might be interesting to only re-run some parts of the report. Config files associated to the parts of the report that should be rerun can be specified via the configFiles parameter. Create the final clinical data report from the Markdown reports

To convert all created Markdown files to HTML, the dedicated function postProcessReport can be used.

4.3 Optimization for big clinical trial data

4.3.1 Optimize the report creation

The creation of the report can be time-consuming for big clinical trial data.

The creation of the report is speed up via the use of the nCores parameter. If specified, the creation of the Markdown file corresponding to each config file, and the creation of the HTML file for each (split) Markdown file are run in parallel.

4.3.2 Optimize the report loading time

The report can take some time to load in the browser for big clinical trial data.

The loading time can be improved by splitting the HTML page into smaller HTML pages, via the split_by parameter. This can be set at a level 7 of sectioning (level 1 corresponds to a chapter), and for a specific part of the report, corresponding to a specific config file.

Example of config file with the use of the split_by parameter

In the example above, the report contains a section titled: ‘2.1 Laboratory profiles’ (level 2 of sectioning via the reportTitleLevel parameter), containing a subsection for each category of laboratory parameter (LBCAT) and a (nested) subsubsection for each laboratory test (LBTEST).
For example, this report can contain the subsubsection: ‘ Alanine Aminotransferase’ with the laboratory profile for the Alanine Aminotransferase laboratory parameter.

This chapter is split at level 4 of sectioning: [level 2 for the title + 2 levels for the loopingVar variables], so the subsubsections containing the content for a laboratory parameter are available in separate HTML pages.

4.3.3 Optimize the report structure

In case a lot of sections with interactive plots are created via the loopingVar parameter, the table of contents can be quite extensive.

An alternative is to create a selection box (instead of a section) to select/filter the data of the plot based on grouping of a specific variable, via the selectVars parameter in the plotting function.

5 Appendix

5.1 Session info

R version 4.2.1 (2022-06-23) Platform: x86_64-pc-linux-gnu (64-bit) Running under: Ubuntu 20.04.4 LTS

Matrix products: default BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/ LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/


attached base packages: [1] stats graphics grDevices utils datasets methods base

other attached packages: [1] plyr_1.8.7 plotly_4.10.0 ggplot2_3.3.6 clinUtils_0.1.1 clinDataReview_1.3.1 knitr_1.40

loaded via a namespace (and not attached): [1] ggrepel_0.9.1 Rcpp_1.0.9 inTextSummaryTable_3.2.0 tidyr_1.2.1 digest_0.6.29 utf8_1.2.2
[7] R6_2.5.1 evaluate_0.16 httr_1.4.4 highr_0.9 pillar_1.8.1 gdtools_0.2.4
[13] rlang_1.0.6 uuid_1.1-0 lazyeval_0.2.2 data.table_1.14.2 jquerylib_0.1.4 DT_0.25
[19] flextable_0.8.2 rmarkdown_2.16 labeling_0.4.2 stringr_1.4.1 htmlwidgets_1.5.4 munsell_0.5.0
[25] compiler_4.2.1 xfun_0.33 systemfonts_1.0.4 pkgconfig_2.0.3 base64enc_0.1-3 htmltools_0.5.3
[31] tidyselect_1.1.2 tibble_3.1.8 bookdown_0.29 jsonvalidate_1.3.2 fansi_1.0.3 viridisLite_0.4.1
[37] dplyr_1.0.10 withr_2.5.0 grid_4.2.1 jsonlite_1.8.2 gtable_0.3.1 lifecycle_1.0.2
[43] magrittr_2.0.3 scales_1.2.1 zip_2.2.1 cli_3.4.1 stringi_1.7.8 cachem_1.0.6
[49] reshape2_1.4.4 farver_2.1.1 xml2_1.3.3 bslib_0.4.0 ellipsis_0.3.2 generics_0.1.3
[55] vctrs_0.4.2 cowplot_1.1.1 tools_4.2.1 forcats_0.5.2 glue_1.6.2 officer_0.4.4
[61] purrr_0.3.4 hms_1.1.2 crosstalk_1.2.0 parallel_4.2.1 fastmap_1.1.0 yaml_2.3.5
[67] colorspace_2.0-3 haven_2.5.1 sass_0.4.2