| 马 涛,依帕拉·伊力哈木,高春洁,等.基于分位数回归的贝叶斯联合模型探究雌二醇纵向变化对乳腺癌患者预后的影响[J].肿瘤学杂志,2025,31(1):36-42. |
| 基于分位数回归的贝叶斯联合模型探究雌二醇纵向变化对乳腺癌患者预后的影响 |
| Impact of Dynamic Change in Estradiol Level on Prognosis of Breast Cancer Patient： a Study Using Bayesian Joint Model Based on Quantile Regression |
| 投稿时间：2024-05-13 |
| DOI：10.11735/j.issn.1671-170X.2025.01.B006 |
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| 中文关键词: 乳腺肿瘤 贝叶斯联合模型 雌二醇 分位数回归 线性分位数混合模型 Cox比例风险模型 生存 |
| 英文关键词:breast neoplasms Bayesian joint model estradiol quantile regression linear quantile mixed model Cox proportional hazards model survival |
| 基金项目:国家自然科学基金资助项目（12061079）；“天山英才”青年科技创新人才培养（2022TSYCCX0108）；新疆自然科学基金项目（2022D01C287） |
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| 中文摘要: |
| 摘 要： ［目的］ 探讨雌二醇（estradiol，E2）水平动态变化与乳腺癌患者生存预后的潜在关联，比较新辅助治疗与无新辅助治疗下乳腺癌患者生存率的差异性。［方法］ 基于2015—2019年新疆医科大学附属肿瘤医院随访的女性乳腺癌患者的临床数据，首先在不同分位数下（■ =0.10，0.25，0.50，0.75）分别建立线性分位数混合模型拟合E2水平的动态变化，并通过赤池信息量准则（akaike information criterion，AIC）与贝叶斯信息准则（Bayesian information criteria，BIC）从中选择最优模型作为联合模型的纵向子模型。其次，基于扩展的Cox比例风险模型建立生存子模型；进一步通过共享随机效应建立纵向与生存数据的贝叶斯分位数联合模型，并通过马尔科夫链蒙特卡洛（Markov Chain Monte Carlo，MCMC）方法估计其关联系数（■）。［结果］ 最优子模型筛选结果显示，■ =0.50时，纵向子模型的AIC与BIC值最小。在 ■ =0.50下构建贝叶斯分位数联合模型。联合模型结果显示，E2水平的动态变化与乳腺癌患者的生存结局显著性相关（■=0.59，HR=1.80，95%CI：1.47~2.24）。新辅助治疗是乳腺癌患者的保护因素（HR=0.155，95%CI：0.047~0.384），能够降低乳腺癌患者84.5%死亡风险。［结论］ 乳腺癌患者E2水平增加与不良生存预后相关，新辅助治疗可降低乳腺癌患者的死亡风险，并改善其生存预后。乳腺癌患者应采取积极治疗手段控制雌二醇水平升高、抑制肿瘤的生长和扩散，从而提高患者的生存率。 |
| 英文摘要: |
| Abstract： ［Objective］ To assess the impact of dynamic changes of estradiol (E2) level on the survival prognosis of breast cancer patients using a Bayesian Joint Model， and to compare the difference of survival of breast cancer with /without neoadjuvant therapy. ［Methods］ Based on clinical data of breast cancer patients treated in the Affiliated Cancer Hospital of Xinjiang Medical University from 2015 to 2019， a linear quantile mixed model was established at different quantiles (■ =0.10， 0.25， 0.50， 0.75) to fit the dynamic changes of E2 levels， and the best longitudinal sub-model was selected by akaike information criterion (AIC) and Bayesian information criteria (BIC). And an extended Cox proportional hazards model was used to establish a survival sub-model. Furthermore， a quantile joint model for longitudinal and survival data was constructed by sharing random effects， and the key joint parameter was estimated using the Markov Chain Monte Carlo method. ［Results］ The quantile joint model at ■ =0.50 was the best one with the smallest AIC and BIC. Hence， the Bayesian quantile joint model at ■ =0.50 was the most appro-priate. The results of the joint model at ■=0.50 showed a significant association between the dynamical changes in E2 level and the survival outcome of breast cancer patients(■=0.59，HR=1.80，95%CI： 1.47~2.24). Neoadjuvant therapy was identified to be an independent prognostic factor for breast cancer patients (HR=0.155， 95%CI： 0.047~0.384)， with reducing the death risk by 84.5%. ［Conclusion］ There is a correlation between elevated levels of estradiol and poor survival in breast cancer patients， and neoadjuvant therapy would significantly reduce the death risk and improve the survival prognosis for breast cancer patients. Therefore， controlling E2 levels would be effective for improving the survival and quality of life of breast cancer patients. |
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