Optimal Partitioning for Linear Mixed Effects Models: Applications to Identifying Placebo Responders.
Optimal Partitioning for Linear Mixed Effects Models: Applications to Identifying Placebo Responders.
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DOI：
10.1198/jasa.2010.ap08713
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发表时间：
2010-01-01
影响因子：
3.7
通讯作者：
Govindarajulu U
中科院分区：
文献类型：
--
作者：
Tarpey T;Petkova E;Lu Y;Govindarajulu U
在临床研究中，一个长期存在的问题是如何区分因药物的特异性作用而有反应的药物治疗对象与对治疗的非特异性（或安慰剂）作用有反应的药物治疗对象。线性混合效应模型通常用于纵向临床试验数据的建模。在本文中，我们提出了一个解决方案，以确定使用线性混合效应模型的最佳分区方法安慰剂应答者的问题。由于纵向研究中的个体结果对应于曲线，最佳划分方法产生一组原型结果概况。最优分划方法可以同时适应连续和离散协变量。将提出的划分策略与生长混合模型方法进行了比较和对比。该方法应用于一项两期抑郁症临床试验，第一阶段的受试者公开接受氟西汀治疗12周，随后是双盲停药阶段，在第一阶段对治疗有反应的患者随机选择继续使用氟西汀或改用安慰剂。将最优划分方法应用于第一阶段，以确定原型结果概况。在研究的第二阶段，使用复发的时间，对分割的数据进行生存分析。最佳划分结果确定了区分受试者是否复发的原型概况，取决于他们是否继续服用药物或随机分配到安慰剂。
A long–standing problem in clinical research is distinguishing drug treated subjects that respond due to specific effects of the drug from those that respond to non-specific (or placebo) effects of the treatment. Linear mixed effect models are commonly used to model longitudinal clinical trial data. In this paper we present a solution to the problem of identifying placebo responders using an optimal partitioning methodology for linear mixed effects models. Since individual outcomes in a longitudinal study correspond to curves, the optimal partitioning methodology produces a set of prototypical outcome profiles. The optimal partitioning methodology can accommodate both continuous and discrete covariates. The proposed partitioning strategy is compared and contrasted with the growth mixture modelling approach. The methodology is applied to a two-phase depression clinical trial where subjects in a first phase were treated openly for 12 weeks with fluoxetine followed by a double blind discontinuation phase where responders to treatment in the first phase were randomized to either stay on fluoxetine or switched to a placebo. The optimal partitioning methodology is applied to the first phase to identify prototypical outcome profiles. Using time to relapse in the second phase of the study, a survival analysis is performed on the partitioned data. The optimal partitioning results identify prototypical profiles that distinguish whether subjects relapse depending on whether or not they stay on the drug or are randomized to a placebo.
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影响因子：
3.7
作者：
COX, DR
通讯作者：
COX, DR
影响因子：
0.8
作者：
LI, LN;FLURY, B
通讯作者：
FLURY, B
影响因子：
15.8
作者：
Kirsch, Irving;Deacon, Brett J.;Huedo-Medina, Tania B.;Scoboria, Alan;Moore, Thomas J.;Johnson, Blair T.
通讯作者：
Johnson, Blair T.
影响因子：
1
作者：
Celeux, G;Martin, O;Lavergne, C
通讯作者：
Lavergne, C
影响因子：
1.7
作者：
Luschgy, H;Pagès, G
通讯作者：
Pagès, G