Cheng Yizi, Brokamp Cole, Manning Erika Rasnick, Kramer Elizabeth L, Ryan Patrick H, Szczesniak Rhonda D, Gecili Emrah
Division of Biostatistics & Epidemiology, Cincinnati Children's Hospital Medical Center, 3333 Burnet Ave (MLC 5041), Cincinnati, OH, 45229, USA.
Department of Pediatrics, University of Cincinnati, Cincinnati, OH, USA.
BMC Med Inform Decis Mak. 2025 Aug 13;25(1):304. doi: 10.1186/s12911-025-03097-2.
Prior research has shown that place-based environmental exposures and community characteristics, known as geomarkers, are associated with accelerated lung function decline and increased mortality in individuals with cystic fibrosis (CF). Although geomarkers have been linked to pulmonary outcomes in other respiratory diseases, it is unknown which have the greatest predictive power for rapid lung function decline in CF.
We adapted an existing statistical procedure, which arranges candidate variables in a k-dimensional hypercube, where the hypercube forms a set of variables for a multi-stage selection process involving complex longitudinal data. We embedded the hypercube within a dynamic prediction model of rapid lung function decline, in order to accommodate complexity in lung function trajectories. This practical approach simultaneously selects a handful of genuinely predictive markers among candidates and accounts for complex correlations in longitudinal marker data. Our method is applied to actual geomarker and lung-function outcomes data from the existing Cystic Fibrosis Patient Registry and Cincinnati Cystic Fibrosis Center datasets.
We applied a 4 × 4 × 4 3-D hypercube to the national and local datasets and selected a subset of geomarkers using p-values from testing coefficients of the association between each geomarker and lung function decline in the dynamic prediction model. Based on the national data analyses, some road density-related geomarkers were selected, including some air pollution-related and greenspace-related variables. Simulations showed the proposed method's variable selection efficacy and robust performance in identifying true predictors, particularly under weak correlation (ρ≤0.6), although performance dipped with stronger correlations (ρ=0.9).
The proposed method is a useful approach for selecting a small set of truly relevant demographic, clinical, and place-based predictors of rapid lung function decline while accounting for the complex correlations inherent in longitudinal lung-function data. We found that selection results differed according to spatial resolution of the geomarkers. Our findings have potential to improve care decisions for people with CF.
先前的研究表明，基于地点的环境暴露和社区特征（即地理标志物）与囊性纤维化（CF）患者的肺功能加速下降和死亡率增加有关。尽管地理标志物已与其他呼吸系统疾病的肺部结局相关联，但对于CF患者肺功能快速下降而言，哪些地理标志物具有最大的预测能力尚不清楚。
我们采用了一种现有的统计程序，该程序将候选变量排列在一个k维超立方体内，其中超立方体形成了一组变量，用于涉及复杂纵向数据的多阶段选择过程。我们将超立方体嵌入到肺功能快速下降的动态预测模型中，以适应肺功能轨迹的复杂性。这种实用方法同时在候选标志物中选择少数真正具有预测性的标志物，并考虑纵向标志物数据中的复杂相关性。我们的方法应用于来自现有囊性纤维化患者登记处和辛辛那提囊性纤维化中心数据集的实际地理标志物和肺功能结局数据。
我们将一个4×4×4的三维超立方体应用于国家和本地数据集，并使用动态预测模型中每个地理标志物与肺功能下降之间关联的测试系数的p值来选择地理标志物的一个子集。基于国家数据分析，选择了一些与道路密度相关的地理标志物，包括一些与空气污染和绿地相关的变量。模拟显示了所提出方法在识别真正预测因子方面的变量选择功效和稳健性能，特别是在弱相关性（ρ≤0.6）情况下，尽管在强相关性（ρ=0.9）时性能有所下降。
所提出的方法是一种有用的方法，可用于选择一小部分真正相关的人口统计学、临床和基于地点的预测因子，以预测肺功能快速下降，同时考虑纵向肺功能数据中固有的复杂相关性。我们发现选择结果因地理标志物的空间分辨率而异。我们的研究结果有可能改善CF患者的护理决策。
