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老年人脉压变化与全因死亡关联的贝叶斯联合模型应用研究

谢纬华 余小金 戴品远 孙金芳 王莉娜 覃玉 武鸣 赵健

谢纬华, 余小金, 戴品远, 孙金芳, 王莉娜, 覃玉, 武鸣, 赵健. 老年人脉压变化与全因死亡关联的贝叶斯联合模型应用研究[J]. 中华疾病控制杂志, 2021, 25(1): 72-77. doi: 10.16462/j.cnki.zhjbkz.2021.01.014
引用本文: 谢纬华, 余小金, 戴品远, 孙金芳, 王莉娜, 覃玉, 武鸣, 赵健. 老年人脉压变化与全因死亡关联的贝叶斯联合模型应用研究[J]. 中华疾病控制杂志, 2021, 25(1): 72-77. doi: 10.16462/j.cnki.zhjbkz.2021.01.014
XIE Wei-hua, YU Xiao-jin, DAI Pin-yuan, SUN Jin-fang, WANG Li-na, QIN Yu, WU Ming, ZHAO Jian. Application of a bayesian joint model for the association of changes in pulse pressure and all-cause mortality in the elderly[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2021, 25(1): 72-77. doi: 10.16462/j.cnki.zhjbkz.2021.01.014
Citation: XIE Wei-hua, YU Xiao-jin, DAI Pin-yuan, SUN Jin-fang, WANG Li-na, QIN Yu, WU Ming, ZHAO Jian. Application of a bayesian joint model for the association of changes in pulse pressure and all-cause mortality in the elderly[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2021, 25(1): 72-77. doi: 10.16462/j.cnki.zhjbkz.2021.01.014

老年人脉压变化与全因死亡关联的贝叶斯联合模型应用研究

doi: 10.16462/j.cnki.zhjbkz.2021.01.014
基金项目: 

国家自然科学基金 81673274

详细信息
    通讯作者:

    余小金,E-mail:xiaojinyu@seu.edu.cn

  • 中图分类号: R181

Application of a bayesian joint model for the association of changes in pulse pressure and all-cause mortality in the elderly

Funds: 

National Natural Science Foundation of China 81673274

More Information
  • 摘要:   目的  以老年人脉压变化与全因死亡的关联研究为背景,探讨贝叶斯联合模型拟合相互关联的纵向结局与生存结局时的应用策略和统计性能,为类似特征资料的分析提供方法学指导。  方法  本研究以自然三次样条函数拟合纵向测量脉压的非线性混合效应模型,并采用B样条法构建全死因生存结局的基线风险函数,通过共享的随机效应关联两个过程建立贝叶斯联合模型,运用Gibbs抽样进行模型参数计算,并将贝叶斯联合模型结果与经典两步法联合模型进行比较。  结果  贝叶斯联合模型结果显示基线脉压越高(α1=0.72, 95% CI: 0.43~1.13),第0~3年、3~6年、6~9年脉压升高越快(α21=0.34, 95% CI: 0.20~0.45); α22=0.45, 95% CI: 0.10~0.75; α23=0.42, 95% CI: 0.24~0.62),老年人全因死亡风险越高。贝叶斯法和两步法联合模型在参数点估计方向上具有一致性,贝叶斯法的区间宽度大于两步法。  结论  当纵向与生存数据存在关联时,贝叶斯联合模型是对纵向与生存数据联合分析合理有效的统计方法。研究显示,老年人基线脉压过高和脉压升高过快均会造成更高的死亡风险。
  • 图  1  老年人脉压水平随时间变化的轨迹

    注:灰色线代表 10位老年人的脉压,橙色线表示总体人群平均脉压。

    Figure  1.  The changing trajectories of pulse pressure in the elderly

    图  2  贝叶斯联合模型与两步法联合模型的协变量参数估计结果比较

    Figure  2.  Comparison of covariable parameter estimation results between Bayesian and two-stage joint model

    表  1  老年健康影响因素跟踪调查数据库研究人群基线情况描述

    Table  1.   Baseline descriptions of the study population in the CLHLS

    预测变量 例数[n (%)] 预测变量 例数[n (%)]
    性别 心脑血管疾病
      男 6 355 (46.41)   是 1 476 (10.78)
      女 7 337 (53.59)   否 12 216 (89.22)
    民族 呼吸系统疾病
      汉族 12 933 (94.48)   是 2 799 (20.44)
      其他民族 759 (5.52)   否 10 893 (79.56)
    年龄(岁) 心理健康水平
      60~ 4 880 (35.64)   较低 6 873 (52.79)
      ≥80 8 812 (64.36)   较高 6 146 (47.20)
    是否受过教育 主食摄入量
      是 5 689 (41.77)   较少 6 866 (50.15)
      否 7 930 (58.22)   较多 6 826 (49.85)
    60岁前职业 水果摄入频率
      脑力劳动为主 1 313 (9.63)   较少 8 083 (59.04)
      体力劳动为主 12 323 (90.37)   较多 5 608 (40.97)
    居住地类型 蔬菜摄入频率
      城镇 6 356 (46.42)   较少 6 689 (48.85)
      乡村 7 336 (53.58)   较多 7 002 (51.14)
    婚姻状态 肉类摄入频率
      未婚、丧偶、分居 9 035 (65.99)   较少 6 954 (50.79)
      已婚并与配偶同居 4 657 (34.01)   较多 6 737 (49.21)
    结婚次数(次) 鱼类摄入频率
      ≤1 11 995 (89.11)   较少 9 051 (66.10)
      ≥2 1 466 (10.89)   较多 4 641 (33.90)
    主要经济来源 鸡蛋摄入频率
      自己 5 046 (36.85)   较少 7 460 (54.49)
      他人 8 646 (63.15)   较多 6 232 (45.52)
    自平经济水平 豆制品摄入频率
      较差或一般 10 385 (76.02)   较少 6 915 (50.50)
      较好 3 276 (23.98)   较多 6 777 (49.49)
    主食类型 腌菜摄入频率
      大米 8 988 (65.64)   较少 7 266 (53.07)
      其他主食 4 704 (34.36)   较多 6 426 (46.94)
    吸烟 白糖摄入频率
      是 4 720 (35.40)   较少 9 089 (66.38)
      否 8 613 (64.60)   较多 4 603 (33.62)
    喝酒 饮茶频率
      是 4 021 (31.24)   较少 6 878 (50.23)
      否 8 850 (68.76)   较多 6 813 (49.77)
    日常锻炼 大蒜摄入频率
      是 4 882 (39.39)   较少 7 775 (56.78)
      否 7 512 (60.61)   较多 5 917 (43.21)
    高血压 日常休闲活动
      是 3 786 (27.65)   较少 7 656 (55.92)
      否 9 906 (72.35)   较多 6 036 (44.08)
    糖尿病 认知功能评分
      是 593 (4.33)   较低 6 539 (47.76)
      否 13 102 (95.67)   较高 7 153 (52.24)
    下载: 导出CSV
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出版历程
  • 收稿日期:  2020-07-11
  • 修回日期:  2020-11-10
  • 刊出日期:  2021-01-10

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