乳腺癌患者肿瘤细胞水平和死亡时间纵向变化的联合建模:以埃塞俄比亚提格雷Ayder综合专科医院为例

Bsrat Tesfay, T. Getinet, E. A. Derso
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摘要

摘要摘要:乳腺癌是世界范围内的重大公共卫生问题,它会导致严重的身体损害和死亡。本研究提出采用联合模型对艾德尔医院乳腺癌患者进行研究。研究的主要动机是利用一种联合模型,考虑到2015年9月至2018年12月同一受试者观察中可能存在的一系列相关结构,有助于了解艾德尔医院内乳腺癌的肿瘤细胞进展。本研究的总体目的是调查乳腺癌患者因乳腺癌至死亡时肿瘤细胞水平纵向变化的风险。以医院为基础,对乳腺癌患者进行回顾性队列研究。采用纵向模型和死亡时间模型联合模型确定乳腺癌患者肿瘤细胞水平在死亡时间纵向变化的风险。这些都是在R版本中使用JM包使用的。关节模型的结果显示,肿瘤细胞的纵向进展与患者的生存率显著相关(关节模型的估计关联参数为0.84,相应的(95% CI: 2.28,2.37)。将该联合模型的参数估计值与独立生存和纵向分析进行比较,我们得出结论:独立分析导致参数估计值偏差。在log(TCL)的进展变化和乳腺癌死亡风险之间有很强的关联。
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Joint modeling of longitudinal change in tumor cell level and time to death of breast cancer patients: In case of Ayder comprehensive specialized Hospital Tigray, Ethiopia
Abstract Abstract: Breast cancer is the major public health problem throughout the world and it results in serious physical damages and death. This work proposes the use of joint model to study breast cancer in patients of Ayder Hospital. The primary motivation is to contribute to the understanding of the tumor cell progression of breast cancer, within Ayder Hospital, using a joint model that takes into account a possible existence of a serial correlation structure within a same subject observations from September 2015 till December 2018. The general aim of this study was to investigate the risk of longitudinal change in tumor cell level on time to death due to breast cancer among breast cancer patients. Hospital-based retrospective cohort study was conducted among breast cancer patients. A joint model of longitudinal and time to death model was used to determine the risk of longitudinal change in tumor cell level on time to death due to breast cancer patients. These were used by using JM package in R version. Results from joint models, showed that the longitudinal Tumor cell progression was signicantly associated with the survival probability of these patients(estimated association parameter(ɑ) in the joint model is 0.84 with corresponding (95% CI: 2.28,2.37). A comparison between parameter estimates obtained in this joint model and independent survival and longitudinal analysis lead us to conclude that independent analysis brings up bias parameter estimates. There is a strong association between the progression change in log(TCL) and risk of mortality due to breast cancer.
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