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Modeling a cross-sectional response variable with longitudinal predictors: an example of pulse pressure and pulse wave velocity

机译:使用纵向预测变量对横截面响应变量进行建模:脉冲压力和脉冲波速度的示例

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摘要

We wish to model pulse wave velocity (PWV) as a function of longitudinal measurements of pulse pressure (PP) at the same and prior visits at which the PWV is measured. A number of approaches are compared. First, we use the PP at the same visit as the PWV in a linear regression model. In addition, we use the average of all available PPs as the explanatory variable in a linear regression model. Next, a two-stage process is applied. The longitudinal PP is modeled using a linear mixed-effects model. This modeled PP is used in the regression model to describe PWV. An approach for using the longitudinal PP data is to obtain a measure of the cumulative burden, the area under the PP curve. This area under the curve is used as an explanatory variable to model PWV. Finally, a joint Bayesian model is constructed similar to the two-stage model.
机译:我们希望对脉搏波速度(PWV)进行建模,作为脉搏压力(PP)的纵向测量结果的函数,该测量是在测量PWV的相同时间和之前进行的。比较了许多方法。首先,我们在线性回归模型中使用与PWV相同的访问次数的PP。此外,我们使用所有可用PP的平均值作为线性回归模型中的解释变量。接下来,应用两阶段过程。使用线性混合效应模型对纵向PP进行建模。在回归模型中使用此建模的PP来描述PWV。使用纵向PP数据的一种方法是获取累积负担的度量,即PP曲线下的面积。曲线下的该区域用作对PWV建模的解释变量。最后,类似于两阶段模型,构造了联合贝叶斯模型。

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