Asuka Nemoto
Graduate School of Public Health, Teikyo University , Japan
Introduction: The likelihood ratio test is typically conducted to evaluate whether a candidate covariate should be included into or excluded from the model in population pharmacokinetic (PPK) analysis. For example, the relationship between a dichotomous characteristic of patients and clearance of a drug to be evaluated. Recently, we proposed a method for a covariate selection in PPK analysis when a sampling schedule does not cover the entire time-concentration curve and hence enough observations were not obtained in a specific time zone that is “information rich” with respect to a PK parameters (e.g. clearance) [1]. Based on the equation derived by Wang [2], we proposed an approximation of deviance that enable to calculate a new statistic, “pseudo” difference of deviances (or OFV, objective function value, in NONMEM), by creating individual components of deviance for any time points added to cover a time region without observations. However, a method to determine a reasonable rejection criterion has been needed to be developed, because the proposed statistic no longer follow the chi-square distribution.
Objectives: To compare the power obtained from the proposed method that uses a parametric bootstrap, with the “true” power using a critical value of a likelihood ratio test determined by a distribution of differences of pseudo OFVs made by simulation under the null hypothesis.
Methods: The blood drug concentration profiles from virtual population were simulated for different sampling designs using a 1-compartment model with first order absorption with inter-individual variability in the apparent distribution volume and in the apparent clearance, assuming exponential error models respectively. The simulation settings are as follows: the number of subjects is 80 and half of subjects have the characteristic that affects clearance of the test drug (i.e. a typical individual value in one half of the subjects was 0.7 times of the other half); a sampling schedule of each scenario was determined by deleting the latter part of a time-concentration curve (1, 3, 6, 9, 12, 18, 24, 36, 48, 60 and 72 h after dosing), intended to dare make an unfavorable situation lacking observations which are information rich about clearance; a sampling schedule of each scenario is same for all subjects. For each of simulated datasets, a difference of pseudo OFV was calculated and simultaneously a rejection criterion was determined from the 5 percentile of the distribution of differences of pseudo OFVs that was created by parametric bootstrap using a set of estimated PPK parameters under the null hypothesis. The power to reject the null hypothesis under the various sampling design was calculated. NONMEM 7.4 and SAS 9.4 were used.
Results: Results of simulation studies showed that the power obtained with the proposed method almost consistent with the “true” power under the study designs evaluated.
Conclusions: With the proposed method, we obtained the rejection criterion that are needed to adopt the robust covariate selection method to real data analysis under the sampling schedule that lacks “information rich” observations. The robust covariate selection method improves the power in the likelihood ratio test about whether a candidate of a covariate contributes to the inter-individual variety of a certain PK parameter.
References:
[1] Nemoto A. A Robust Covariate Selection Method for the Limited Sampling Design in Population Pharmacokinetic Analysis. The 6th International Symposium on Biopharmaceutical Statistics. August 26-30, 2019. Kyoto Japan.
[2] Wang Y. Derivation of Various NONMEM Estimation Methods. J Pharmacokinet Pharmacodyn. 2007;34(5):575-93.
Reference: PAGE () Abstr 9576 [www.page-meeting.org/?abstract=9576]
Poster: Methodology - Covariate/Variability Models