Biomedical subjects

Chuanpu Hu

Publications and source records attributed to Chuanpu Hu.

4 recordsLinked to original sources

PubMed2007

Estimating the predictive quality of dose-response after model selection.

Prediction of dose-response is important in dose selection in drug development. As the true dose-response shape is generally unknown, model selection is frequently used, and predictions based on the final selected model. Correctly assessing the quality of the predictions requires accounting for the uncertainties caused by the model selection process, which has been difficult. Recently, a new approach called data perturbation has emerged. It allows important predictive characteristics be computed while taking model selection into consideration. We study, through simulation, the performance of data perturbation in estimating standard error of parameter estimates and prediction errors. Data perturbation was found to give excellent prediction error estimates, although at times large Monte Carlo sizes were needed to obtain good standard error estimates. Overall, it is a useful tool to characterize uncertainties in dose-response predictions, with the potential of allowing more accurate dose selection in drug development. We also look at the influence of model selection on estimation bias. This leads to insights into candidate model choices that enable good dose-response prediction.

Bias↗

PubMed2005

Variability of target-controlled infusion is less than the variability after bolus injection.

BACKGROUND: Target-controlled infusion (TCI) drug delivery systems deliver intravenous drugs based on pharmacokinetic models. TCI devices administer a bolus, followed by exponentially declining infusions, to rapidly achieve and maintain pseudo-steady state drug concentrations in the plasma or at the site of drug effect. Many studies have documented the prediction accuracy of TCI devices. The authors' goal was to apply linear systems theory to characterize the relation between the variability in concentrations achieved with TCI devices and the variability in concentrations after intravenous bolus injection. METHODS: The authors developed a mathematical model of the variability of any arbitrary method of drug delivery, based on the variability with intravenous bolus injection or the variability with an arbitrary infusion regimen. They tested the model in a simulation of 1,000 patients receiving propofol by simple bolus injection, conventional infusion, or a TCI device. The authors then examined an experimental data set for the same behavior. RESULTS: The variability of any arbitrary infusion regimen, including TCI, is bounded by the variability after bolus injection. This is observed in the simulation and experimental data sets as well. CONCLUSION: TCI devices neither create nor eliminate biologic variability. For any drug described by linear pharmacokinetic models, no infusion regimen, including TCI, can have higher variability than that observed after bolus injection. The median performance of TCI devices should be reasonably close to the prediction of the device. However, the overall spread of the observations is an intrinsic property of the drug, not the TCI delivery system.

Animals↗

PubMed2004

Statistical issues in a modeling approach to assessing bioequivalence or PK similarity with presence of sparsely sampled subjects.

Drug development at different stages may require assessment of similarity of pharmacokinetics (PK). The common approach for such assessment when the difference is drug formulation is bioequivalence (BE), which employs a hypothesis test based on the evaluation of a 90% confidence interval for the ratio of average pharmacokinetic (PK) parameters. The role of formulation effect in BE assessment is replaced by subject population in PK similarity assessment. The traditional approach for BE requires that the PK parameters, primarily AUC and Cmax, be obtained from every individual. Unfortunately in many clinical circumstances, some or even all of the individuals may be sparsely sampled, making the individual evaluation difficult. In such cases, using models, particularly population models, becomes appealing. However, conducting an appropriate statistical test based on population modeling in a form consistent, at least in principle, with traditional 90% confidence interval approach is not so straightforward as it may appear. This manuscript proposes one such approach that can be applied to sparse sampling situations. The approach aims to maintain, as much as possible, the appropriateness of the hypothesis test. It is applied to data from clinical studies to address a need in drug development for assessment of PK similarity in different populations.

Area Under Curve↗

PubMed2003

A joint model for nonlinear longitudinal data with informative dropout.

Subject withdrawal from a study (also called dropout, or right censoring), is common in late phase clinical trials. A number of methods dealing with dropouts have been used in practice, the most common being "last observation carried forward" (LOCF). Many of these methods, including LOCF, can result in biased estimates of the efficacy or potency of the drug, especially in the modeling context. If the likelihood of dropout is correlated to the underlying unobserved data, the dropout is informative and should not be ignored in the modeling process. The topic of informative dropout in the context of longitudinal data has received much attention in the statistical literature, in the setting of linear and generalized linear models. We extend the approach to nonlinear models. The dropout hazard, as well as the longitudinal data, is modeled parametrically. Parameters are estimated by maximizing the approximate joint likelihood as implemented in the software NONMEM. Using data from actual clinical trials, we explore the impact of the dropout model on the ability of the joint model to predict observed longitudinal data patterns.

Clinical Trials as Topic↗