Chien Li-Chu, Chang Li-Ying, Shen Chung-Wei
Center for Fundamental Science, Kaohsiung Medical University, Kaohsiung, Taiwan, ROC.
Department of Mathematics, National Chung Cheng University, Chia-Yi, Taiwan, ROC.
Pharm Stat. 2023 Jan;22(1):79-95. doi: 10.1002/pst.2261. Epub 2022 Aug 23.
We propose a model selection criterion for correlated survival data when the cluster size is informative to the outcome. This approach, called Resampling Cluster Survival Information Criterion (RCSIC), uses the Cox proportional hazards model that is weighted with the inverse of the cluster size. The RCSIC based on the within-cluster resampling idea takes into account the possible variability of the within-cluster subsampling and the possible informativeness of cluster sizes. The RCSIC allows for easy execution for the within-cluster resampling idea without a large number of resamples of the data. In contrast with the traditional model selection method in survival analysis, the RCSIC has an additional penalization for the within-cluster subsampling variability. Our simulations show the satisfactory results where the RCSIC provides a more robust power for variable selection in terms of clustered survival analysis, regardless of whether informative cluster size exists or not. Applying the RCSIC method to a periodontal disease studies, we identify the tooth loss in patients associated with the risk factors, Age, Filled Tooth, Molar, Crown, Decayed Tooth, and Smoking Status, respectively.
当聚类大小对结果具有信息量时，我们提出了一种针对相关生存数据的模型选择标准。这种方法称为重采样聚类生存信息准则（RCSIC），它使用了以聚类大小的倒数加权的Cox比例风险模型。基于聚类内重采样思想的RCSIC考虑了聚类内子采样的可能变异性以及聚类大小的可能信息量。RCSIC允许轻松执行聚类内重采样思想，而无需对数据进行大量重采样。与生存分析中的传统模型选择方法相比，RCSIC对聚类内子采样变异性有额外的惩罚。我们的模拟显示了令人满意的结果，即无论是否存在信息量丰富的聚类大小，RCSIC在聚类生存分析中为变量选择提供了更强健的功效。将RCSIC方法应用于一项牙周病研究，我们分别确定了与年龄、补牙、磨牙、牙冠、龋齿和吸烟状况等风险因素相关的患者牙齿脱落情况。
