SAAM II PopKinetics Features

PopKinetics with SAAM II

PopKinetics analyzes populations of individual subjects, where each subject in the population is represented by one SAAM II Compartmental Study file. PopKinetics is compatible with SAAM II v2.0 and higher and runs on Microsoft Windows 98 and above.


  • 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
  • Incorporates a Global Tool to rapidly modify model and data attributes for each subject in the population
    • The Reference File provides a mechanism to modify the settings in every Subject File in a population without having to make the changes manually to each file; in other words, certain settings in the analysis files generated are inherited automatically from the Reference File; to change the data weighting from relative to absolute for the analysis, for example, simply change the setting in the Reference File and re-save the file. During the next Compute (or Check), PopKinetics automatically includes the new value(s) in each file that it generates from the originals in the new analysis; note that the original files are not modified
  • 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.
  • Includes detailed on-line help with examples


  • 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. Suppose one wonders if the statistics are adequate and the confidence intervals for the parameters are well calculated. Monte Carlo simulation allows us to test such a hypothesis, provided the statistics of the measurement error are known. It is based on simulating a large number of synthetic data sets and calculating the average estimate for all the synthetic data sets.
      • If we wish to test the parameter confidence intervals, we are interested only in the effect of the measurement error
      • We do not allow the parameters to vary (there is no between-individual variation)
      • The PopKinetics Simulator screen would appear similar to the screen in the image above
      • Since we’re treating the parameters as constants for this simulation, the warning in the image above will appear: [This is “OK” for a Monte Carlo simulation]; once the simulated population has been generated, a quick STS analysis provides statistics that can be compared to the original SAAM II study statistics