背景:肾细胞癌(RCC)由于其存活率低,仍然是全球健康问题。本研究旨在探讨医学决定因素和社会经济状况对RCC患者生存结局的影响。我们分析了监测下记录的41,563例RCC患者的生存数据,流行病学,和2012年至2020年的最终结果(SEER)计划。
方法:我们采用了竞争风险模型,假设不同风险下的RCC患者的生存期遵循Chen分布。该模型解释了与生存时间以及死亡原因相关的不确定性,包括失踪的死因.对于模型分析,我们利用贝叶斯推断,获得了累积发生率函数(CIF)和特定原因危险等各种关键参数的估计值.此外,我们采用贝叶斯假设检验来评估多因素对RCC患者生存时间的影响.
结果:我们的研究结果表明,肾癌患者的生存时间受性别的显著影响,收入,婚姻状况,化疗,肿瘤大小,和偏侧性。然而,我们观察到种族和起源对患者的生存时间没有显著影响。CIF图表明,与收入因素相对应的死亡原因发生率存在许多重要差异,婚姻状况,种族,化疗,和肿瘤大小。
结论:本研究强调了各种医学和社会经济因素对RCC患者生存时间的影响。此外,这也证明了在贝叶斯范式下竞争风险模型在RCC患者生存分析中的实用性。该模型提供了一个强大而灵活的框架来处理丢失的数据,这在患者信息可能不完整的现实生活中特别有用。
BACKGROUND: Renal cell carcinoma (RCC) remains a global health concern due to its poor survival rate. This study aimed to investigate the influence of medical determinants and socioeconomic status on survival outcomes of RCC patients. We analyzed the survival data of 41,563 RCC patients recorded under the Surveillance, Epidemiology, and End Results (SEER) program from 2012 to 2020.
METHODS: We employed a competing risk model, assuming lifetime of RCC patients under various risks follows Chen distribution. This model accounts for uncertainty related to survival time as well as causes of death, including missing cause of death. For model analysis, we utilized Bayesian inference and obtained the estimate of various key parameters such as cumulative incidence function (CIF) and cause-specific hazard. Additionally, we performed Bayesian hypothesis testing to assess the impact of multiple factors on the survival time of RCC patients.
RESULTS: Our findings revealed that the survival time of RCC patients is significantly influenced by gender, income, marital status, chemotherapy, tumor size, and laterality. However, we observed no significant effect of race and origin on patient\'s survival time. The CIF plots indicated a number of important distinctions in incidence of causes of death corresponding to factors income, marital status, race, chemotherapy, and tumor size.
CONCLUSIONS: The study highlights the impact of various medical and socioeconomic factors on survival time of RCC patients. Moreover, it also demonstrates the utility of competing risk model for survival analysis of RCC patients under Bayesian paradigm. This model provides a robust and flexible framework to deal with missing data, which can be particularly useful in real-life situations where patients information might be incomplete.