Table 1.
The most common Pmetrics functions used to manipulate data before and after running NPAG, IT2B, or the Simulator
| Pmetrics function | Comment |
|---|---|
| Manipulate data files | |
| PMreadMatrix | Read a Pmetrics data file into R |
| PMcheckMatrix | Check an R data frame for errors which would cause a run to fail |
| PMwriteMatrix | Write an R data frame to a new Pmetrics data file |
| Process data | |
| makeAUC | Calculate AUC over any time interval from a variety of inputs using the trapezoidal approximation |
| makeCov† | Make a data frame (class PMcov*) containing Bayesian posterior parameter estimates for each subject with their mean covariate values, suitable for covariate analysis with linear or non- linear regression for example |
| makeCycle† | Make a list (class PMcycle*) containing cycle values for log-likelihood, Akaike and Bayesian Information Criteria, gamma/lambda (fixed-effect process noise multiplier of assay error), mean/median/SD values for each random model parameter normalized to the cycle 1 value, all to assist with assessment of convergence |
| makeFinal† | A list (class PMfinal*) with summary statistics for the final cycle parameter estimates (e.g. mean, median, covariance, etc.) after an NPAG or IT2B run, additionally with the non-parametric joint density after an NPAG run |
| makeNCA | Run a non-compartmental analysis on the full, predicted, first-dose profiles from an NPAG run (see makePost). This will calculate AUC by the trapezoidal rule from time 0 to the time of the next dose (or all time points for a single-dose study) and AUMC over the same interval. Extrapolation to infinity of AUC and AUMC, using the last 6 predictions in the interval is made, along with reporting of clearance, maximum concentration, time to maximum, and half-life. |
| makeOP† | A data frame (class PMop*) with subject identifiers, times, observations, predictions (based on population or individual posterior parameter distributions) and errors, all suitable for observed vs. predicted and residual plots |
| makePost† | Create predictions for each subject and output at user-specified intervals using the mean, median, or mode of individual Bayesian posterior parameter distributions |
| Plot Pmetrics objects* | |
| plot.PMcov | The relationship between any two columns (i.e. Bayesian posterior parameters and covariates) of a PMcov object. |
| plot.PMcycle | The data in a PMcycle object vs. cycle number |
| plot.PMfinal | Univariate or bivariate marginal final cycle parameter value distributions in a PMfinal object |
| plot.PMmatrix | Raw time-observation data from a data file read by the PMreadMatrix command, with a variety of options, including joining observations with line segments, including doses, overlaying plots for all subjects or separating them, including individual posterior predictions, color coding according to groups and more |
| plot.PMop | Observed vs. population or individual Bayesian posterior predicted data or residual plots (see below). |
| plot.PMsim | Simulated time-concentration profiles from Simulator output via SIMparse, overlaid as individual curves or summarized by customizable quantiles (e.g. 5th, 25th, 50th, 75th and 95th percentiles); inclusion of observations in a population can be used to return a visual and numerical predictive check |
| plot.PMdiag | Generates a prediction discrepancy (pd) normal quantile-quantile (Q-Q) plot, pd histogram, pd vs. time plot, and a pd vs. prediction plot to visualize results of simulation-based internal model diagnostics accessed with the PMdiag command |
| Model selection and diagnostics | |
| PMcompare | Compares any number of NPAG and/or IT2B runs on the basis of final cycle log- likelihood, Akaike and Bayesian Information Criteria, whether convergence was achieved, the root mean squared error (RMSE) of observations minus predictions, based on population and individual Bayesian posterior parameter estimates, and observed vs. predicted plots |
| plot.PMop (with residual option) | Three panels: 1) weighted residuals (observed - predicted) vs. time; 2) weighted residuals vs. predictions; 3) a histogram of residuals with optional superimposed normal curve, the mean of the weighted residuals (expected to be 0), the probability that it is different from 0 by chance, and three tests of normality for the residuals: D’Agostino,17 Shapiro-Wilk, and Kolmogorov-Smirnof |
| PMdiag | Use the Simulator to create a list with pd (prediction discrepancy) data suitable for plotting with plot.PMdiag, above, and for internal model validation |
*
R is an object-oriented language. Therefore objects are assigned classes that have associated methods. For example, all plotting routines in Pmetrics are simply accessed using the command plot(…) rather than, for example, plot.OP(…). This makes it far easier for users, who do not have to remember which plot routine to call for a given object.
†
These objects are automatically created at the end of a run and loaded with the NPload() or ITload() commands. Note, however, that makeNCA and makePost are only available for use on output from an NPAG run.