Smooth nonparametric maximum likelihood estimation for population pharmacokinetics, with application to quinidine.
Marie Davidian,A. Ronald Gallant +1 more
TL;DR: The seminonparametric (SNP) method, popular in the econometrics literature, is proposed for use in population pharmacokinetic analysis and a graphical modelbuilding strategy based on the SNP method is described.
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Abstract: The seminonparametric (SNP) method, popular in the econometrics literature, is proposed for use in population pharmacokinetic analysis. For data that can be described by the nonlinear mixed effects model, the method produces smooth nonparametric estimates of the entire random effects density and simultaneous estimates of fixed effects by maximum likelihood. A graphical modelbuilding strategy based on the SNP method is described. The methods are illustrated by a population analysis of plasma levels in 136 patients undergoing oral quinidine therapy.
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Figures
Table II. Parameter Estimates 
Table III. Population Characteristics of the Random Effects 
Table I. Optimization Results, Case 2, Covariates: Weight, at-acid Glycoprotein Concentration, Creatinine Clearance 
Figure 3, and Figure 4. In Table I, the inclusion of weight, creatinine clearance, and aI-acid glycoprotein concentration in the C1equation is strongly supported by all criteria in all specifications, K = 0,2,3. For the models with covariates, the conservative BIC criterion selects the normal (K = 0), and HQ and AIC criteria select the K = 2 specification. 
Figure 3, and Figure 4. In Table I, the inclusion of weight, creatinine clearance, and aI-acid glycoprotein concentration in the C1equation is strongly supported by all criteria in all specifications, K = 0,2,3. For the models with covariates, the conservative BIC criterion selects the normal (K = 0), and HQ and AIC criteria select the K = 2 specification. 
Fig. 4. Inter-individual regression graphics, Case 2, covariates: weight, 1-acid glycoprotein concentration, creatinine clearance. Empirical Bayes estimates of inter-individual random effects for clearance (upper panel) and volume (lower panel) plotted against initial values of candidate covariates and quinidine concentration. Plots against continuous variables such as age are scatter plots with least squares lines superimposed; the extra line for albumin concentration has unmeasured (zero) values excluded. Plots against categorical variables such as dosage form are boxplots: A horizontal line is drawn in the box at the median, the upper and lower ends of the box are at the upper and lower quartiles, vertical lines go up and down from the median to 1.5 times the interquartile range, extreme points are plotted by themselves, and least squares lines through the medians are superimposed.
Citations
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