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1.  Comparison of stepwise covariate model building strategies in population pharmacokinetic-pharmacodynamic analysis 
AAPS PharmSci  2002;4(4):68-79.
The aim of this study was to compare 2 stepwise covariate model-building strategies, frequently used in the analysis of pharmacokinetic-pharmacodynamic (PK-PD) data using nonlinear mixed-effects models, with respect to included covariates and predictive performance. In addition, the effects of stepwise regression on the estimated covariate coefficients wise regression on the estimated covariate coefficients were assessed. Using simulated and real PK data, covariate models were built applying (1) stepwise generalized additive models (GAM) for identifying potential covariates, followed by backward elimination in the computer program NONMEM, and (2) stepwise forward inclusion and backward elimination in NONMEM. Different versions of these procedures were tried (eg, treating different study occasions as separate individuals in the GAM, or fixing a part of the parameters when the NONMEM procedure was used). The final covariate models were compared, including their ability to predict a separate data set or their performance in cross-validation. The bias in the estimated coefficients (selection bias) was assessed. The model-building procedures performed similarly in the data sets explored. No major differences in the resulting covariate models were seen, and the predictive performances overlapped. Therefore, the choice of model-building procedure in these examples could be based on other aspects such as analyst-and computer-time efficiency. There was a tendency to selection bias in the estimates, although this was small relative to the overall variability in the estimates. The predictive performances of the stepwise models were also reasonably good. Thus, selection bias seems to be a minor problem in this typical PK covariate analysis.
doi:10.1208/ps040427
PMCID: PMC2751316  PMID: 12645999
stepwise model building; covariate analysis; GAM; NONMEM; selection bias
2.  Nonlinearity detection: Advantages of nonlinear mixed-effects modeling 
AAPS PharmSci  2000;2(3):114-123.
The purpose of this study was to address the question of whether the use of nonlinear mixed-effect models has an impact on the detection and characterization of nonlinear processes (pharmacokinetic and pharmacodynamic) in rich data obtained from a few subjects. Simulations were used to assess the difference between applying population analysis, ie, nonlinear mixed-effects models as implemented in NONMEM, and the standard 2-stage (STS) method as the data analysis method for detection and characterization of nonlinearities. Three situations were considered, 2 pharmacokinetic and 1 pharmacodynamic. Both the first-order (FO) and FO conditional estimation (FOCE) algorithms were used for the population analyses. Within each situation, rich data were simulated for 8 subjects at multiple dose levels. The true nonlinear model and a simpler linear model were fit to each data set using each of the STS, FO, and FOCE methods. Criteria were prespecified to determine when each data analysis method detected the true nonlinear model. For all 3 simulated situations, the application of population analysis with the FOCE algorithm enabled the detection and characterization of the true nonlinear models in at least a 4-fold lower dose level than the STS approach. For both of the pharmacokinetic settings, population analysis with the FO algorithm performed much more poorly than the STS approach. The superior detection and characterization of nonlinearities provided by population analysis with the FOCE algorithm should allow drug developers to better predict and define how a drug should be used in clinical practice in such situations.
doi:10.1208/ps020332
PMCID: PMC2761142  PMID: 11741248
Population Analysis; Nonlinear Mixed-Effects Modeling; Nonlinear Pharmacokinetics and Pharmacodynamics; Study Design

Results 1-2 (2)