RsNLME 1.1.1 Update
RsNLME 1.1
Known Issues
June 2022: RsNLME 1.1.1 release
November 2021: RsNLME 1.1 release
RsNLME 1.1.1 release corrects the issue where the posthoc table was missing if numIterations = 0 for model execution (RSNLME-2): The following files have been updated:
R Package: Certara.NLME8_1.2.1.tar.gz
NLME-Engine: NLME-Engine-21.11.2.exe (Windows)
NLME-Engine: NLME-Engine-21.11.2.zip (Linux)
NLME Engine
New “type” argument for LL statements enables generation of appropriate summary statistics worksheets for VPC mode (PRN-652): In PML code, the type argument is now available for specifying an observation variable’s type. This argument can be set to cont, cat, count, or event and ensures the proper output worksheets will be generated for VPC mode.
Simple and Simulation tables include all five ID columns (PRN-645): NLME supports up to 5 ID columns for the input data. To take this into account, five ID columns are added for all simple and simulation tables.
New column ‘LLOQ’ is added to the output of predcheck output (PRN-726): To facilitate the computations of VPC data in R packages, the LLOQ column is added to the predcheck0.csv output file if the model has BQL observation data specified.
Certara.RsNLME
Additional steady state dosing can now be specified using the optional “SSOffset” argument of “addSteady” (PRN-317): This argument produces the “ssoffcol” statement in cols1.txt.
Columns mapped as covariates in the model object are now checked for validity (PRN-629): If data is loaded to a model object and covariate model terms are mapped to data columns, an automatic check is performed regarding data column reliability. A Warning/Error is given in cases where there is inappropriate data.
Initial Estimates Shiny application now uses ggplot2 (PRN-537): The estimatesUI initial estimates Shiny application in the Certara.RsNLME package has been updated to use the ggplot2 plotting library and to support plot faceting.
Command line support for the following has been added to the RsNLME package:
Distributed delay (PRN-618)
Supply class initializer arguments for execution params directly inside execution functions using ellipses (PRN-709)
Data mapping can now be performed without column mapping inside built-in model functions by supplying data argument and setting columnMap = FALSE (PRN-717).
Support for new syntax in mmdl:
Dosing cycles (PRN-633)
Covariate levels and labels (PRN-805)
Certara.Xpose.NLME
New covariate model diagnostic functions have been added to the ‘Certara.Xpose.NLME’ package (PRN-541): New covariate plot functions include support for ETAs, parameters, and residuals plotted against a continuous or categorical covariate.
RsNLME.ModelBuilder
Additional functionality has been added to the RsNLME.ModelBuilder Shiny application:
Administration-absorption parameters for distributed delay (PRN-773)
Dosing cycle specification (PRN-785)
Covariate labels (PRN-805)
Certara.RsNLME.ModelExecutor
Additional functionality has been added to the RsNLME.ModelExecutor Shiny application:
Plot convergence data after model execution (PRN-580)
Return estimation arguments and table statements to mmdl in Pirana (PRN-704)
Add sort column selections for model execution (PRN-423)
Certara.ModelResults
Addition of new Shiny application, Model Results (PRN-620): The ‘Certara.ModelResults’ Shiny application in installed as an R package and used to generate and report model diagnostics plots and tables. The application includes functionality to generate corresponding R code from the xpose, ggplot2, and flextable libraries, given operations performed in the GUI.
Certara.VPCResults
Addition of new Shiny application, VPC Results (PRN-691): The ‘Certara.VPCResults’ Shiny application is installed as an R package and used to parameterize, plot, and report Visual Predictive Checks (VPC). The application includes functionality to generate corresponding R code from the tidyvpc and ggplot2 libraries, given operations performed in the GUI.
Additional information for Certara R packages that are accessed through Pirana is now available as pkgdown websites (PRN-838):
Certara.ModelResults https://certara.github.io/R-model-results/
Certara.RsNLME https://certara.github.io/R-RsNLME/
Certara.RsNLME.ModelBuilder https://certara.github.io/R-RsNLME-model-builder/
Certara.RsNLME.ModelExecutor https://certara.github.io/R-RsNLME-model-executor/
Certara.VPCResults https://certara.github.io/R-VPCResults/
Certara.Xpose.NLME https://certara.github.io/R-Xpose-NLME/
Issues Corrected
NLME Engine
All interoccasion omegas have standard errors in the output (PRN-623): Previously, the output only contained the standard errors (SEs) for omegas related to the first occasion. Now, SEs are included for all interoccasion omegas.
An issue that prevented using more than five observation variables has been resolved (PRN-545): Previously, it was impossible to run a model with more than five observation variables using the QRPEM engine. This issue is fixed in RsNLME 1.1.
An issue involving the failure of parsing when more than one stratification variable is specified has been resolved (PRN-614).
Certara.Xpose.NLME
Non-time-based models now work correctly (PRN-300): Previously, in a non-time-based model, if the name of a covariate in the model was the same as the corresponding one in the input dataset, the covariate would not be listed as a covariate in the database.
For models involving reset, the original dataset appended in the generated data is now correct (PRN-303): Previously, the generated data showed data for all occasions as being exactly the same as the ones corresponding to the first occasion.
Individual plot legends now properly display the line type and color (PRN-307).
Certara.RsNLME
Issue where column named “X_ID” in the data used as subject ID was incorrectly used for subjects accounting has been resolved (PRN-507).
A reported issue where executing a large NLME job on a grid led to the master node for the grid to run out of memory has been resolved (PRN-538).
NLME now accepts negative estimates as initial estimates for the model executed (PRN-543/CS00211446).
Certara.RsNLME.ModelExecutor
The Sort Columns option now works as expected and is now available in the Individual modeling mode (PRN-542).
The NLME Engine installer must be run as an administrator or the installation will fail (RSNLME-346): If an error is encountered during installation, check that the person installing has administrator privileges.
NLME engine returns a negative value for the standard deviation of residual error variable (PRN-540): If such a situation occurs, then the absolute value of the standard deviation should be considered as the final estimate.