Ahn Jae Eun, Karlsson Mats O, Dunne Adrian, Ludden Thomas M
Pharmacometrics R & D, ICON Development Solutions, Ellicott City, MD, USA.
J Pharmacokinet Pharmacodyn. 2008 Aug;35(4):401-21. doi: 10.1007/s10928-008-9094-4. Epub 2008 Aug 7.
To evaluate the likelihood-based methods for handling data below the quantification limit (BQL) using new features in NONMEM VI.
A two-compartment pharmacokinetic model with first-order absorption was chosen for investigation. Methods evaluated were: discarding BQL observations (M1), discarding BQL observations but adjusting the likelihood for the remaining data (M2), maximizing the likelihood for the data above the limit of quantification (LOQ) and treating BQL data as censored (M3), and like M3 but conditioning on the observation being greater than zero (M4). These four methods were compared using data simulated with a proportional error model. M2, M3, and M4 were also compared using data simulated from a positively truncated normal distribution. Successful terminations and bias and precision of parameter estimates were assessed.
For the data simulated with a proportional error model, the overall performance was best for M3 followed by M2 and M1. M3 and M4 resulted in similar estimates in analyses without log transformation. For data simulated with the truncated normal distribution, M4 performed better than M3.
Analyses that maximized the likelihood of the data above the LOQ and treated BQL data as censored provided the most accurate and precise parameter estimates.
利用NONMEM VI中的新功能评估基于似然性的方法处理低于定量限（BQL）的数据。
选择具有一级吸收的二室药代动力学模型进行研究。评估的方法有：舍弃BQL观测值（M1），舍弃BQL观测值但调整其余数据的似然性（M2），最大化定量限（LOQ）以上数据的似然性并将BQL数据视为删失数据（M3），以及与M3类似但以观测值大于零为条件（M4）。使用比例误差模型模拟的数据对这四种方法进行比较。还使用从正截断正态分布模拟的数据对M2、M3和M4进行比较。评估成功终止情况以及参数估计的偏差和精密度。
对于用比例误差模型模拟的数据，总体性能以M3最佳，其次是M2和M1。在无对数转换的分析中，M3和M4得出的估计值相似。对于用截断正态分布模拟的数据，M4的表现优于M3。
最大化LOQ以上数据的似然性并将BQL数据视为删失数据的分析提供了最准确和精确的参数估计。
