Justin J. Wilkins (1), E. Niclas Jonsson (2)
(1) SGS Exprimo NV, Mechelen, Belgium; (2) Pharmetheus AB, Uppsala, Sweden
Reproducibility is the cornerstone of scientific research, but is nonetheless a challenging area in pharmacometric data analysis. The large number of intermediate steps required, often involving multiple versions of datasets, combined with a mixture of software tools and the substantial quantity of results that must be tracked and summarized renders traceability an onerous and time-consuming business.
The concept of “reproducible research” is that the final product of scientific research is not just the text of a report or research article, but should also include the full computational environment used to produce the results, including all the associated code and data – and that this bundle of data and scripts should be shared with others who wish to reproduce these results. Although this is not often possible in pharmacometrics, given that data are usually confidential and that it may not be practical to reproduce hundreds of model fits, we can apply the process of reproducible research to our activities as far as possible to ensure that traceability is maintained.
Although there are many approaches that may be taken to adopting this principle, we shall focus on the combination of R, knitr and LaTeX. These tools together enable the end-to-end scripting of data file creation, capture of results from external software tools and subsequent analyses, and can automate the creation of publication-quality reports, articles and slide decks.
We shall provide a live demonstration of our implementation of this process in a production context, using a real dataset [1].
References:
[1] Grasela TM, Donn SM (1985). Neonatal Population Pharmacokinetics of Phenobarbital Derived from Routine Clinical Data. Dev Pharmacol Ther 8: 374-383.
The example material for the software demonstration is here.
Reference: PAGE 22 () Abstr 2996 [www.page-meeting.org/?abstract=2996]
Poster: Software Demonstration