Segmented mixed models with random changepoints: a maximum likelihood approach with application to treatment for depression study
Segmented mixed models with random changepoints: a maximum likelihood approach with application to treatment for depression study
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DOI：
10.1177/1471082x13504721
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发表时间：
2014-08-01
影响因子：
1
通讯作者：
Dimidjian, Sona
中科院分区：
文献类型：
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作者：
Muggeo, Vito M. R.;Atkins, David C.;Dimidjian, Sona
我们提出了一个简单而有效的迭代过程来估计分段的混合模型的可能性为基础的框架。每个模型参数（包括变点）都允许有随机效应和协变量。该方法是实用的，并避免了相关的非线性混合效应模型估计的计算负担。一个传统的线性混合模型与适当的协变量，占变点是我们的估计算法的关键。我们通过模拟和使用来自随机临床试验的数据来说明该方法，该随机临床试验的重点是随着时间的推移抑郁症状的变化，其特征是显示出两个独立的变化阶段。
We present a simple and effective iterative procedure to estimate segmented mixed models in a likelihood based framework. Random effects and covariates are allowed for each model parameter, including the changepoint. The method is practical and avoids the computational burdens related to estimation of nonlinear mixed effects models. A conventional linear mixed model with proper covariates that account for the changepoints is the key to our estimating algorithm. We illustrate the method via simulations and using data from a randomized clinical trial focused on change in depressive symptoms over time which characteristically show two separate phases of change.