Population pharmacokinetics bootstrapping

Furthermore, when alternative approaches are applied in computing parameter estimates, the question to be addressed here is Do these other approaches yield similar parameter and random effects estimates and conclusions An example of addressing this second point would be estimating the parameters of a population pharmacokinetic (PPK) model by the standard maximum likelihood approach and then confirming the estimates by either constructing the profile likelihood plot (i.e., mapping the objective function), using the bootstrap (4, 9) to estimate 95% confidence intervals, or the jackknife method (7, 26, 27) and bootstrap to estimate standard errors of the estimate (4, 9). When the relative standard errors are small and alternative approaches produce similar results, then we conclude the model is reliable. 

E. I. Ette and L. C. Onyiah, Estimating inestimable standard errors in population pharmacokinetic studies the bootstrap with winsorization. Ear J Drug Metab Pharmacokinet 27 213-224 (2002). 

Bootstrapping is the resampling with replacement method that has the advantage of using the entire data set. It has been demonstrated to be useful in PMM validation (1,3, 22) and has the same advantages as do other internal validation methods in that it obviates the need for collecting data from a test population. Bootstrapping has been applied to population pharmacokinetic (PPK) model development, stability check and evaluation, and bias estimation (1-3, 25). 

Ette (1) provides an example of the application of bootstrapping to PMM budding, specifically to a population pharmacokinetic (PPK) model. In this study it was desired that the deterministic model (one-compartment versus two-compartment) and the covariates for inclusion be known with a high degree of certainty (1, 3). 

D. Bootstrapped data or samples (had 323 subjects (i = 1, 2, 3,..., 323) subjects data) (Do), on which the developed population pharmacokinetic model was based) were drawn with replacement from the observed data (Do) observed data could either appear in the bootstrap samples (D,) once, more than one time, or not at all. For each bootstrap data set, the structure was retained but the coefficients and the intercept were reestimated. 

TABLE 15.2 Bootstrap Estimates for Various Population Pharmacokinetic Parameters Compared to the NONMEM Generated Parameters... 

A. Yafune and M. Ishiguro, Bootstrap approach for constrncting confidence intervals for population pharmacokinetic parameters II. A bootstrap modification of standard two stage method for phase I trial. Stat Med 18 601-612 (1999). 

Gibiansky, E., Gibiansky, L., and Bramer, S. Comparison of NONMEM, bootstrap, jackknife, and profiling parameter estimates and confidence intervals for the aripiprazole population pharmacokinetic model. Presented at American Association of Pharmaceutical Scientists Annual Meeting, Boston MA, 2001. 

Yafune, A. and Ishiguro, M. Bootstrap approach for constructing confidence intervals for population pharmacokinetic parameters. I A use of bootstrap standard error. Statistics in Medicine 1999 18 581-599. 

The bootstrap has been used in pharmacokinetics sporadically on a largely theoretical basis and has not really been implemented on a routine basis, except in the case of validating population models. Bonate (1993), which was later improved upon by Jones et al. (1996), showed how the bootstrap can be applied to obtain CIs for individual drug concentrations, which could be of importance in therapeutic drug monitoring. Bonate later... 

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