Optimal Design in Hierarchical Random Effect Models for Individual Prediction with Application in Precision Medicine
Optimal Design in Hierarchical Random Effect Models for Individual Prediction with Application in Precision Medicine
复制标题
个体预测分层随机效应模型的优化设计及其在精准医学中的应用
DOI：
10.1007/s42519-020-00090-y
复制
发表时间：
2020
影响因子：
0.6
通讯作者：
Schwabe R.
中科院分区：
文献类型：
--
作者：
Prus M;Benda N;Schwabe R.
分层随机效应模型在临床研究和其他领域有不同的用途。一般而言，主要侧重于与预期治疗效果或上级所有单位(例如，许多环境中的受试者)之间的群体差异有关的总体参数。总体参数估计的最优设计在许多模型中都得到了很好的应用。然而，各个单元的预测的最佳设计可能是不同的。确定了其中可能感兴趣的单个预测的几个设置。在这篇文章中，我们为个体预测确定最优设计，例如在多组试验中或在多个不同亚群中调查新治疗的试验中，并在治疗分配方面将它们与传统的平衡设计进行比较。我们的研究表明，在不相关的簇截取和簇处理的情况下，如果处理效果与残差相比变化很大，那么最优分配远远不平衡，应该招募更多的受试者参加主动(新)处理。然而，效率损失可能是有限的，导致在均衡分配的情况下预测个别预测时，样本量会适度增加。
Hierarchical random effect models are used for different purposes in clinical research and other areas. In general, the main focus is on population parameters related to the expected treatment effects or group differences among all units of an upper level (e.g. subjects in many settings). Optimal design for estimation of population parameters are well established for many models. However, optimal designs for the prediction for the individual units may be different. Several settings are identified in which individual prediction may be of interest. In this paper, we determine optimal designs for the individual predictions, e.g. in multi-cluster trials or in trials that investigate a new treatment in a number of different subpopulations, and compare them to a conventional balanced design with respect to treatment allocation. Our investigations show that in the case of uncorrelated cluster intercepts and cluster treatments the optimal allocations are far from being balanced if the treatment effects vary strongly as compared to the residual error and more subjects should be recruited to the active (new) treatment. Nevertheless, efficiency loss may be limited resulting in a moderate sample size increase when individual predictions are foreseen with a balanced allocation.
影响因子：
2.3
作者：
Lemme, Francesca;van Breukelen, Gerard J. P.;Berger, Martijn P. F.
通讯作者：
Berger, Martijn P. F.
DOI：
10.1111/rssb.12105
发表时间：
2016-01-01
影响因子：
5.8
作者：
Prus, Maryna;Schwabe, Rainer
通讯作者：
Schwabe, Rainer