基于S3C-Latent算法的癌症患者生活质量因素间的因果建模

Yohani Setiya Rafika Nur, R. Rahmadi, C. Effendy
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引用次数: 0

摘要

背景:癌症患者可能会经历身体和非身体问题,如社会心理、精神和情绪问题,这些问题会影响生活质量。以往关于生活质量的研究大多采用多变量分析。据我们所知,目前还没有研究集中在代表癌症患者生活质量的因素之间的潜在因果关系,这在试图改善生活质量时非常重要。目的:本研究旨在建立癌症与生活质量之间的因果关系模型。方法:本研究采用S3C-Latent方法估计各因素之间的因果模型关系。S3C-Latent方法将结构方程模型(SEM)、多目标优化方法和稳定性选择方法相结合,估计出一个稳定且简洁的因果模型。结果:共发现9个因果关系,即从身体到整体健康,信度得分为0.73,到工作状态,信度得分为1;从情绪到整体健康,信度得分为0.71,到工作状态,信度得分为0.82;从恶心、食欲不振、呼吸困难、失眠、食欲不振、便秘到工作状态,信度得分为0.76;1;0.61;0.76;0.72;0.70,分别。此外,本研究发现,15个因素之间的关联(强关系,不能从数据单独确定因果方向),信度评分范围从0.65到1。结论:估算模型与前人研究结果一致。该模型有望为医疗保健提供者提供基于证据的建议,以设计提高癌症患者生活质量的策略。对于未来的研究,我们建议在模型中加入更多的变量,以获得对问题的更广泛的看法。
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Causal Modeling Between Factors on Quality of Life in Cancer Patients Using S3C-Latent Algorithm
Background: Cancer patients can experience both physical and non-physical problems such as psychosocial, spiritual, and emotional problems, which impact the quality of life. Previous studies on quality of life mostly have employed multivariate analyses. To our knowledge, no studies have focused yet on the underlying causal relationship between factors representing the quality of life of cancer patients, which is very important when attempting to improve the quality of life. Objective: The study aims to model the causal relationships between the factors that represent cancer and quality of life. Methods: This study uses the S3C-Latent method to estimate the causal model relationships between the factors. The S3C-Latent method combines Structural Equation Model (SEM), a multi objective optimization method, and the stability selection approach, to estimate a stable and parsimonious causal model. Results: There are nine causal relations that have been found, i.e., from physical to global health with a reliability score of 0.73, to performance status with a reliability score of 1, from emotional to global health with a reliability score of 0.71, to performance status with a reliability score of 0.82, from nausea, loss of appetite, dyspnea, insomnia, loss of appetite and from constipation to performance status with reliability scores of 0.76; 1; 0.61; 0.76; 0.72; 0.70, respectively. Moreover, this study found that 15 associations (strong relation where the causal direction cannot be determined from the data alone) between factors with reliability scores range from 0.65 to 1. Conclusion: The estimated model is consistent with the results shown in previous studies. The model is expected to provide evidence-based recommendation for health care providers in designing strategies to increase cancer patients’ life quality. For future research, we suggest studies to include more variables in the model to capture a broader view to the problem.
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