Use of prior information to stabilize a population data analysis
Use of prior information to stabilize a population data analysis
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DOI：
10.1023/a:1022972420004
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发表时间：
2002-12-01
影响因子：
2.5
通讯作者：
Beal, SL
中科院分区：
文献类型：
--
作者：
Gisleskog, PO;Karlsson, MO;Beal, SL
当使用复杂的群体药代动力学/药效学模型对新数据建模时，可能没有足够的信息来获得所有参数的估计值。在这种情况下，以前研究的信息也可以用来帮助稳定估计。使用模拟数据，我们探索了三种不同的方法来做到这一点。(i)一些参数值被固定到从早期数据获得的估计值。(ii)以前的数据与当前的数据相结合。(iii)基于当前数据的目标函数通过表示从先前数据获得的汇总信息的惩罚函数来增强。最后一种方法类似于贝叶斯先验的使用。当方法（ii）的组合数据集非常大并导致大量计算时间时，或者当早期数据不容易获得时，它可能特别有用。该方法采用了两种不同类型的罚函数。通过算例分析，这三种方法都能得到稳定的估计。方法（ii）和（iii）给出了相似的结果参数和标准误估计，特别是相对于固定效应参数。对于假设检验，用方法（i）获得的结果是非常有问题的。用方法（iii）得到的结果也有问题，但它们不那么严重，当已知早期数据的设计时，它们可以通过使用计算机密集型模拟试验程序来校正。
When modeling new data with a complex population pharmacokinetic/pharmacodynamic model, there may not be sufficient information to obtain estimates of all parameters. In this case information from previous studies can also be used to help stabilize estimation. Using simulated data, we explored three different ways to do this. (i) Some parameter values were fixed to estimates obtained from earlier data. (ii) The earlier data were combined with the current data. (iii) The objective function based on the current data was augmented by a penalty function expressing summary information obtained from the earlier data. This last method is similar to the use of a Bayesian prior. It may be particularly useful when either the combined data set of method (ii) is very large and leads to large computation times or when the early data are not readily available. With this method, two different types of penalty functions were used. With our examples, the three methods all resulted in stabilized estimation. Methods (ii) and (iii) gave similar results for parameter and standard error estimation, especially with respect to fixed effects parameters. For hypothesis testing, results obtained with method (i) are very problematic. There are also problems with the results obtained with method (iii), but they are much less severe, and when the design for the earlier data is known, they can be corrected by using a computer-intensive simulation test procedure.