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/