We consider joint selection of fixed and random effects in general mixed-effects models. The interpretation of estimated mixed-effects models is challenging since changing the structure of one set of effects can lead to different choices of important covariates in the model. We propose a stepwise selection algorithm to perform simultaneous selection of the fixed and random effects. It is based on Bayesian Information criteria whose penalties are adapted to mixed-effects models. The proposed procedure performs model selection in both linear and nonlinear models. It should be used in the low-dimension setting where the number of ovariates and the number of random effects are moderate with respect to the total number of observations. The performance of the algorithm is assessed via a simulation study, which includes also a comparative study with alternatives when available in the literature. The use of the method is illustrated in the clinical study of an antibiotic agent kinetics.

中文翻译：

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混合效应模型中联合协变量和随机效应选择的迭代算法


我们考虑在一般混合效应模型中联合选择固定效应和随机效应。估计混合效应模型的解释具有挑战性，因为改变一组效应的结构可能会导致模型中重要协变量的不同选择。我们提出了一种逐步选择算法来同时选择固定效应和随机效应。它基于贝叶斯信息标准，其惩罚适应混合效应模型。所提出的过程在线性和非线性模型中执行模型选择。它应该用在低维环境中，其中卵巢的数量和随机效应的数量相对于观察总数来说是中等的。该算法的性能通过模拟研究进行评估，其中还包括与文献中可用的替代方案的比较研究。该方法的使用在抗生素药物动力学的临床研究中得到说明。