ABSTRACT

Longitudinal studies of rapid disease progression often rely on noisy biomarkers; the underlying longitudinal process naturally varies between subjects and within an individual subject over time; the process can have substantial memory in the form of within-subject correlation. Cystic fibrosis lung disease progression is measured by changes in a lung function marker (FEV1), such as a prolonged drop in lung function, clinically termed rapid decline. Choosing a longitudinal model that estimates rapid decline can be challenging, requiring covariate specifications to assess drug effect while balancing choices of covariance functions. Two classes of longitudinal models have recently been proposed: segmented and stochastic linear mixed effects (LMEs) models. With segmented LMEs, random changepoints are used to estimate the timing and degree of rapid decline, treating these points as structural breaks in the underlying longitudinal process. In contrast, stochastic LMEs, such as random walks, are locally linear but utilize continuously changing slopes, viewing bouts of rapid decline as localized, sharp changes. We compare commonly utilized variants of these approaches through an application using the Cystic Fibrosis Foundation Patient Registry. Changepoint modeling had the worst fit and predictive accuracy but certain covariance forms in stochastic LMEs produced problematic variance estimates.

摘要

快速疾病进展的纵向研究通常依赖于嘈杂的生物标志物。随着时间的推移，各个受试者之间以及各个受试者内部的潜在纵向过程自然会发生变化；该过程可以以主题内相关的形式拥有大量的记忆。囊性纤维化性肺疾病的进展是通过肺功能标记（FEV1）的变化来衡量的，例如肺功能的长时间下降（临床上称为快速下降）。选择一个估计快速下降的纵向模型可能具有挑战性，需要协变量规范来评估药物效果，同时平衡协方差函数的选择。最近已经提出了两类纵向模型：分段和随机线性混合效应（LME）模型。使用分段的LME，随机变化点用于估计快速下降的时间和程度，并将这些点视为基础纵向过程中的结构性断裂。相反，随机LME（例如随机游动）局部呈线性，但利用连续变化的坡度，将快速下降的回合视为局部的急剧变化。我们通过使用囊性纤维化基金会患者注册表的应用程序比较了这些方法的常用变体。变更点建模具有最差的拟合度和预测准确性，但是随机LME中的某些协方差形式产生了有问题的方差估计。将快速下降的回合视为局部的急剧变化。我们通过使用囊性纤维化基金会患者注册表的应用程序比较了这些方法的常用变体。变更点建模具有最差的拟合度和预测准确性，但是随机LME中的某些协方差形式产生了有问题的方差估计。将快速下降的回合视为局部的急剧变化。我们通过使用囊性纤维化基金会患者注册表的应用程序比较了这些方法的常用变体。变更点建模具有最差的拟合度和预测准确性，但是随机LME中的某些协方差形式产生了有问题的方差估计。