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首页> 美国卫生研究院文献>CPT: Pharmacometrics Systems Pharmacology >A novel method based on unbiased correlations tests for covariate selection in nonlinear mixed effects models: The COSSAC approach
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A novel method based on unbiased correlations tests for covariate selection in nonlinear mixed effects models: The COSSAC approach

机译：基于非偏见相关性测试的新型方法用于非线性混合效应模型的协变量选择：Cossac方法

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摘要

Building a covariate model is a crucial task in population pharmacokinetics and pharmacodynamics in order to understand the determinants of the interindividual variability. Identifying a good covariate model usually requires many runs. Several procedures have been proposed in the past to automatize this task. The most commonly used is Stepwise Covariate Modeling (SCM). Here, we present a novel stepwise method based on statistical tests between individual parameters sampled from their conditional distribution and the covariates. This strategy, called the COnditional Sampling use for Stepwise Approach based on Correlation tests (COSSAC), makes use of the information contained in the current model to choose which parameter‐covariate relationship to try next. This strategy greatly reduces the number of covariate models tested, while retaining on its search path the models improving the log‐likelihood (LL). In this article, we detail the COSSAC method and its implementation in Monolix, and evaluate its performance. The performance was assessed by comparing COSSAC to the traditional SCM method on 17 representative data sets. For the large majority of cases (15 out of 17), the final covariate model is identical (11 cases) or very similar (4 cases with LL differences less than 3.84) with both procedures. Yet, COSSAC requires between 2 to 20 times fewer runs than SCM. This represents a decisive speed up, especially for models that take long to run and would not be tractable using the SCM method.
机译：建立协变量模型是人口药代动力学和药效动物动力学的关键任务，以了解互动变异性的决定因素。识别良好的协变态模型通常需要许多运行。过去已经提出了若干程序，以自动化这项任务。最常用的是逐步的协变量建模（SCM）。在这里，我们提出了一种基于从其条件分布和协变量采样的各个参数之间的统计测试的新逐步方法。这种策略，称为基于相关测试（Cossac）的逐步方法的条件采样用途，利用当前模型中包含的信息来选择哪个参数 - 协调关系，以尝试接下来。该策略大大减少了测试的协变量模型的数量，同时保留其搜索路径，提高日志似然（LL）的模型。在本文中，我们详细介绍了Cossac方法及其在蒙罗利克斯的实施，并评估其性能。通过将Cossac与17个代表性数据集的传统SCM方法进行比较来评估性能。对于大多数病例（17分），最终的协变化模型是相同的（11例）或非常相似（4例，LL差异小于3.84）。然而，Cossac需要比SCM更少的运行率较少2到20倍。这代表了一种决定性的速度，特别是对于需要长时间运行的模型，并且不会使用SCM方法进行贸易。

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