- Standard Two-Stage (STS) and Iterated Two-Stage (ITS) parametric population analysis methods
- Operates directly on SAAM II single-subject models
- Full SAAM II functionality in population setting
- Allows population analysis of complex (multi-compartment) models; simple models analyzed quickly and easily
- Fast Setup for population analysis (point and click)
- No programming or pseudo code required
- Setup independent of model complexity
- Analysis setup easily modified
- Experimental protocol can vary between subjects
- Rapidly modify model and data attributes for each subject in the population.
- Requires few assumptions to begin population analyses
- User’s notes can be included in Analysis Files to document particular items of interest in an analysis: setup, conclusions, etc.
- Computations proceed automatically in populations with constant, fixed, and random effects
- Alerts users when there are problems in analyzing data; provides interactive error control; direct integration with SAAM II allows for speedy data and model error identification and correction
- Confidence Intervals (shown above) on population parameters to indicate precision with which population parameters are estimated; there is no mechanism in the Standard Two-Stage, Iterated Two-Stage, or Bayesian parameter computations that can compute confidence intervals directly; the confidence interval for each parameter is computed after a completed analysis by generating a number of simulated populations and selecting appropriate values
- Specifically, Confidence Intervals are determined by discarding the highest and lowest parameter values from the computed values for the populations; sufficient populations must be generated to allow at least one highest and one lowest parameter value to be discarded; to compute the 90% confidence interval, for example, requires a minimum of 20 populations; after the highest and lowest values are discarded, 18 of 20 population values remain, giving (18 / 20) * 100 = 90%
- Progress Display during analysis (see image below)
- Analysis results may be plotted using General plot or Samples and Data plot (see images below) to examine frequently used results or listed in Tables
- Results (see images below) may be exported to other applications or printed to a file for additional analysis or reporting – flexible text reporting built in
- Detailed Logs (see images below) are provided to examine events that occur during an analysis to allow user to verify that the analysis proceeded as intended
Powerful Simulation Capabilities
- Simulates Populations with variability in data and model parameters
- The PopKinetics simulator produces a simulated population based on a SAAM II Compartmental model; it can generate any number of Subject Files containing Random, Fixed and Constant Effects; PopKinetics uses the settings and values specified in the simulator window and the Reference File from the main PopKinetics window as the Base File; the simulator can generate a population containing variability in the parameters, noise in the data, or both; this provides great flexibility in determining the characteristics of the population.
- Simulates Clinical Trials to determine effect of varying dosing regimens
- Performs Monte Carlo Simulations that can be used to test single-subject models. Monte Carlo simulation can be used to conduct sensitivity analysis on variables and calculate better confidence intervals for an individual analysis than those available from asymptotic statistics like those available from the Statistics Window in SAAM II.