RESUMEN
Objective Comparing the performance of the two nonparametric Bayesian methods for benchmark dose estimating under different dose response data,then comparing them with traditional parametric methods.Methods Introduce the basic principle of the nonparametric Bayesian method based on weighted process and stochastic process separately,then compared the estimations through simulate study and instance analysis.Results The simulate study shows that the posterior estimates were reasonably close to the target true BMD value for the two nonparametric methods,and NBP2 is more desirable compared to NPB1.The nine examples indicate that the BMD estimates from the nonparametric approaches generally fall into or very near the interval of those obtained from BMDS and nonparametric approaches tend to produce lower BMDLs than the parametric modeling approaches.Conclusion The posterior estimates were reasonably close to the target true BMD value for the two nonparametric methods,especially when standard parametric models fail to fit to the data adequately.The NPB2 method is slightly bet-ter than the NPB1 method in the aspect of estimation result and the software operation speed.
RESUMEN
The estimation of 90% parametric confidence intervals (CIs) of mean AUC and Cmax ratios in bioequivalence (BE) tests are based upon the assumption that formulation effects in log-transformed data are normally distributed. To compare the parametric CIs with those obtained from nonparametric methods we performed repeated estimation of bootstrap-resampled datasets. The AUC and Cmax values from 3 archived datasets were used. BE tests on 1,000 resampled datasets from each archived dataset were performed using SAS (Enterprise Guide Ver.3). Bootstrap nonparametric 90% CIs of formulation effects were then compared with the parametric 90% CIs of the original datasets. The 90% CIs of formulation effects estimated from the 3 archived datasets were slightly different from nonparametric 90% CIs obtained from BE tests on resampled datasets. Histograms and density curves of formulation effects obtained from resampled datasets were similar to those of normal distribution. However, in 2 of 3 resampled log (AUC) datasets, the estimates of formulation effects did not follow the Gaussian distribution. Bias-corrected and accelerated (BCa) CIs, one of the nonparametric CIs of formulation effects, shifted outside the parametric 90% CIs of the archived datasets in these 2 non-normally distributed resampled log (AUC) datasets. Currently, the 80~125% rule based upon the parametric 90% CIs is widely accepted under the assumption of normally distributed formulation effects in log-transformed data. However, nonparametric CIs may be a better choice when data do not follow this assumption.