Fellow NONMEM users, NONMEM users are well aware that creation of a NONMEM dataset can be a complicated task, often requiring many hours or days. PLT Data is a graphical interface designed to facilitate that task. Source data can be in a variety of formats (SAS7BDAT, XPT, CSV, TXT, tab-delimited, pipe-delimited). PLT Data reads these files, saving all output in CSV format and provides the user with information about the content of each file. The user then populates fields in the interface, directing PLT Data how to process the dataset. For example, the user identifies which file contains Cp data, column headers for each of date/time, concentration, etc., and how date/time are formatted. Similar steps are applied to the file containing dosing data and, if available, demographics, vital signs, and laboratory values. PLT data can even link files, one of which contains concentrations, the other containing sample times. PLT Data then constructs a dataset, formatted for NONMEM, containing the core information that the user expects and creates a series of graphics (e.g., by subject, by dose group, composite, time-after-dose).
PLT Data also calculates time-after-dose, number of doses, dose #, and a variety of covariates such as GFR (using several user-selectable formulae), "elderly", lean body mass, and BMI. If LOQ is provided, PLT Data can create an EVID column based on Beal's Method 5 or 6. PLT Data also attempts to translate covariates in text form into numerics, e.g., if race appears in the source data as "White", "black", Caucasian", "W", or "B", PLT Data attempts to map these to numbers (and informs the user as to how that was accomplished). PLT Data can add records to the dataset with EVID=2; these records provide additional predictions, thereby allowing the display of graphics better representing the predicted Cp profile. This is but a small subset of the many things that can be included in a dataset. PLT Data also summarizes the data (number of doses / subject; total dose / subject; observations / subject) and each categorical or continuous covariate. In addition, PLT Data provides a record of every step that it executes, thereby ensuring traceability and reproducibility. The engine for PLT Data is R (open-source software available at R-project.org). Other than installation of R, under most circumstances, the user does not need to be facile with R. However, in some instances, construction of more complicated datasets requires that the user write a few lines of R code (examples are provided). PLT Data is in the final stages of beta testing, having been tested on > 30 datasets. It is available for free at www.PLTsoft.com; an installer, examples, and a manual are provided. Details of what can be accomplished with PLT Data are available at PLTsoft.com. I look forward to people using PLT Data. If you encounter problems (as one expects in the final stages of beta testing), I will fix them. And, I encourage feedback from users as to how to improve the interface and code. Dennis Dennis Fisher MD P < (The "P Less Than" Company) Phone: 1-866-PLessThan (1-866-753-7784) Fax: 1-866-PLessThan (1-866-753-7784) www.PLessThan.com <http://www.plessthan.com/>
