健康指数指标筛选的因果推断方法

刘新辉; 李洪凯; 王丽洁; 刘爱玲; 齐越; 孙珊珊; 张蓝方; 季怀君; 刘贵元; 赵欢; 姜轶男; 李静宜; 宋成村; 于鑫; 杨柳; 于进超; 冯虎; 杨福俊; 薛付忠

doi:10.16462/j.cnki.zhjbkz.2022.10.012

文章导航 > 中华疾病控制杂志 > 2022 > 26(10): 1180-1186

刘新辉, 李洪凯, 王丽洁, 刘爱玲, 齐越, 孙珊珊, 张蓝方, 季怀君, 刘贵元, 赵欢, 姜轶男, 李静宜, 宋成村, 于鑫, 杨柳, 于进超, 冯虎, 杨福俊, 薛付忠. 健康指数指标筛选的因果推断方法[J]. 中华疾病控制杂志, 2022, 26(10): 1180-1186. doi: 10.16462/j.cnki.zhjbkz.2022.10.012

引用本文:

LIU Xin-hui, LI Hong-kai, WANG Li-jie, LIU Ai-ling, QI Yue, SUN Shan-shan, ZHANG Lan-fang, JI Huai-jun, LIU Gui-yuan, ZHAO Huan, JIANG Yi-nan, LI Jing-yi, SONG Cheng-cun, YU Xin, YANG Liu, YU Jin-chao, FENG Hu, YANG Fu-jun, XUE Fu-zhong. Causal inference methodology for the screening of indicators for health indices[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2022, 26(10): 1180-1186. doi: 10.16462/j.cnki.zhjbkz.2022.10.012

Citation:

LIU Xin-hui, LI Hong-kai, WANG Li-jie, LIU Ai-ling, QI Yue, SUN Shan-shan, ZHANG Lan-fang, JI Huai-jun, LIU Gui-yuan, ZHAO Huan, JIANG Yi-nan, LI Jing-yi, SONG Cheng-cun, YU Xin, YANG Liu, YU Jin-chao, FENG Hu, YANG Fu-jun, XUE Fu-zhong. Causal inference methodology for the screening of indicators for health indices[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2022, 26(10): 1180-1186. doi: 10.16462/j.cnki.zhjbkz.2022.10.012

引用本文:

Citation:

LIU Xin-hui, LI Hong-kai, WANG Li-jie, LIU Ai-ling, QI Yue, SUN Shan-shan, ZHANG Lan-fang, JI Huai-jun, LIU Gui-yuan, ZHAO Huan, JIANG Yi-nan, LI Jing-yi, SONG Cheng-cun, YU Xin, YANG Liu, YU Jin-chao, FENG Hu, YANG Fu-jun, XUE Fu-zhong. Causal inference methodology for the screening of indicators for health indices[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2022, 26(10): 1180-1186. doi: 10.16462/j.cnki.zhjbkz.2022.10.012

健康指数指标筛选的因果推断方法

doi: 10.16462/j.cnki.zhjbkz.2022.10.012

刘新辉^{1, 2},
李洪凯^{1, 2},
王丽洁^{1, 2},
刘爱玲³,
齐越⁴,
孙珊珊⁴,
张蓝方⁵,
季怀君⁶,
刘贵元⁷,
赵欢⁸,
姜轶男⁹,
李静宜⁹,
宋成村⁵,
于鑫⁴,
杨柳⁵,
于进超⁷,
冯虎⁴,
杨福俊^4, ,,
薛付忠^1, ,

1.
250012 济南, 山东大学齐鲁医学院公共卫生学院生物统计学系
2.
250003 济南, 国家健康医疗大数据研究院
3.
264299 威海, 威海市立医院呼吸与危重症医学科
4.
264299 威海, 威海市立医院肿瘤综合治疗科
5.
264299 威海, 威海市立医院化疗科
6.
264299 威海, 威海市立医院胸外科
7.
264299 威海, 威海市立医院影像科
8.
256699 滨州, 滨州医学院第二临床医学院肿瘤综合科
9.
264299 威海, 威海市立医院放疗科

基金项目:

国家重点研发计划 2020YFC2003500

详细信息

通讯作者:
杨福俊, E-mail: 58500775@qq.com

薛付忠, E-mail: xuefzh@sdu.edu.cn

中图分类号: R181.2
计量
- 文章访问数: 300
- HTML全文浏览量: 340
- PDF下载量: 98
- 被引次数: 0
出版历程
- 收稿日期: 2022-05-09
- 修回日期: 2022-08-29
- 刊出日期: 2022-10-10

Causal inference methodology for the screening of indicators for health indices

1.
Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan 250012, China
2.
National Institute of Health Data Science of China, Jinan 250003, China
3.
Department of Pulmonary and Critical Care Medicine, Weihai Municipal Hospital, Weihai 264299, China
4.
Department of Oncology, Weihai Municipal Hospital, Weihai 264299, China
5.
Department of Chemotherapy, Weihai Municipal Hospital, Weihai 264299, China
6.
Department of Thoracic Surgery, Weihai Municipal Hospital, Weihai 264299, China
7.
Department of Radiology, Weihai Municipal Hospital, Weihai 264299, China
8.
Department of Oncology, The Second Medical College of Binzhou Medical University, Binzhou 256699, China
9.
Department of Radiotherapy, Weihai Municipal Hospital, Weihai 264299, China

Funds:

National Key Research and Development Program of China 2020YFC2003500

More Information

Corresponding author: YANG Fu-jun, E-mail: 58500775@qq.com; XUE Fu-zhong, E-mail: xuefzh@sdu.edu.cn

摘要

摘要: 健康指数体系的构建与发展对于推动健康中国目标的实现具有重要的战略意义。从现实世界数据入手，通过一系列的因果推断方法，筛选和确定对健康/疾病结局具有确凿因果关系且可干预的健康指数指标，从而为健康/疾病管理提供更贴近实践、更有价值的现实世界证据是至关重要的。本文针对健康指数构建的循证医学需求，介绍了目前常用的现实世界研究中人群水平评估的因果推断方法，为健康指数指标筛选提供方法支撑。
- 现实世界数据 /
- 健康指数 /
- 因果推断 /
- 混杂偏倚
Abstract: The construction and development of the health index system have important strategic significance for promoting the realization of the Healthy China initiative. Starting from the real-world data, it is essential to screen indicators for health indices that are definite causes of diseases and can be prevented through a series of causal inference methods. This can provide valuable real-world evidence that is closer to the practice of health/disease management. According to the need for evidence-based medicine for health index construction, this paper introduces population-level causal effect estimation methods that are widely used in real-world studies, aiming at providing methodological support for the screen of indicators for health index.
- Real-world data /
- Health index /
- Causal inference /
- Confounding bias

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图 1 倾向性评分匹配前肺癌生存K-M曲线图

Figure 1. Kaplan-Meier survival curve for lung cancer before propensity score matching

下载: 全尺寸图片幻灯片

图 2 倾向性评分匹配前服用/未服用铂类药物组偏好得分分布图

Figure 2. The distribution of the preference score grouped by the usage of platinum drugs before propensity score matching

下载: 全尺寸图片幻灯片

图 3 倾向性评分匹配前/后协变量平衡检测图

Figure 3. Covariate balance that shows the absolute standardized difference of mean before and after propensity score matching

下载: 全尺寸图片幻灯片

图 4 倾向性评分匹配后肺癌生存K-M曲线图

Figure 4. Kaplan-Meier survival curve for lung cancer after propensity score matching

下载: 全尺寸图片幻灯片

图 5 肺癌患者服用铂类药物群体因果效应分析结果

Figure 5. The estimation of population-level average causal effects of using platinum drugs on lung cancer

下载: 全尺寸图片幻灯片

表 1 服用铂类药物和未服用铂类药物的基线特征描述[n(%)]

Table 1. Characteristic description of baseline indicators grouped by the usage of platinum drugs [n(%)]

变量	未服用铂类药物 (n=1 235)	服用铂类药物 (n=945)	合计(N=2 180)	t/χ²值	P值
年龄(x±s, 岁)	65.33±9.26	61.01±8.45	63.46±9.17	11.20	< 0.001
年龄分组(岁)				115.21	< 0.001
< 50	67(5.43)	78(8.25)	145(6.65)
50~ < 60	231(18.70)	292(30.90)	523(23.99)
60~ < 70	519(42.02)	433(45.82)	952(43.67)
≥70	418(33.85)	142(15.03)	560(25.69)
性别				18.67	< 0.001
女	510(41.30)	304(32.17)	814(37.34)
男	725(58.70)	641(67.83)	1 366(62.66)
婚姻				0.03	0.862
已婚	1 229(99.51)	939(99.37)	2 168(99.45)
非已婚	6(0.49)	6(0.63)	12(0.55)
医保类型				52.09	< 0.001
城乡居民基本医疗保险	768(62.19)	518(54.81)	1 286(58.99)
城镇职工基本医疗保险	327(26.48)	373(39.47)	700(32.11)
其他社会保险	140(11.34)	54(5.71)	194(8.90)
吸烟状况				9.58	0.008
不吸烟	716(57.98)	485(51.32)	1 201(55.09)
戒烟	217(17.57)	192(20.32)	409(18.76)
吸烟	302(24.45)	268(28.36)	570(26.15)
饮酒状况				10.77	0.001
无饮酒	820(66.40)	562(59.47)	1 382(63.39)
饮酒	415(33.60)	383(40.53)	798(36.61)
肺癌家族史				0.83	0.361
无	1 118(90.53)	867(91.75)	1 985(91.06)
有	117(9.47)	78(8.25)	195(8.94)
是否机会性筛查				0.00	1.000
机会性筛查	250(20.24)	191(20.21)	441(20.23)
症状体征	985(79.76)	754(79.79)	1 739(79.77)
肿瘤部位				2.40	0.662
右上	403(32.63)	313(33.12)	716(32.84)
左上	302(24.45)	216(22.86)	518(23.76)
右下	239(19.35)	172(18.20)	411(18.85)
左下	200(16.19)	173(18.31)	373(17.11)
右中	91(7.37)	71(7.51)	162(7.43)
是否腺癌				0.49	0.484
腺癌	891(72.15)	668(70.69)	1 559(71.51)
非腺癌	344(27.85)	277(29.31)	621(28.49)
T分期				33.51	< 0.001
T1	125(10.12)	148(15.66)	273(12.52)
T2	407(32.96)	367(38.84)	774(35.50)
T3	238(19.27)	129(13.65)	367(16.83)
T4	465(37.65)	301(31.85)	766(35.14)
N分期				28.86	< 0.001
N0	227(18.38)	108(11.43)	335(15.37)
N1	94(7.61)	102(10.79)	196(8.99)
N2	462(37.41)	412(43.60)	874(40.09)
N3	452(36.60)	323(34.18)	775(35.55)
M分期				0.11	0.738
M0	606(49.07)	456(48.25)	1 062(48.72)
M1	629(50.93)	489(51.75)	1 118(51.28)
TNM分期				0.40	0.819
Ⅱ期	159(12.87)	126(13.33)	285(13.07)
Ⅲ期	447(36.19)	330(34.92)	777(35.64)
Ⅳ期	629(50.93)	489(51.75)	1 118(51.28)
有无手术				21.61	< 0.001
穿刺活检	983(79.60)	670(70.90)	1 653(75.83)
手术切除	252(20.40)	275(29.10)	527(24.17)
有无放疗				301.85	< 0.001
无	1 068(86.48)	497(52.59)	1 565(71.79)
有	167(13.52)	448(47.41)	615(28.21)
是否服用非铂类的化疗药				110.85	< 0.001
无	1 097(88.83)	945(100.00)	2 042(93.67)
有	138(11.17)	0(0.00)	138(6.33)
是否服用靶向治疗药物				142.10	< 0.001
无	875(70.85)	430(45.50)	1 305(59.86)
有	360(29.15)	515(54.50)	875(40.14)
是否服用免疫治疗药物				134.81	< 0.001
无	1 221(98.87)	816(86.35)	2 037(93.44)
有	14(1.13)	129(13.65)	143(6.56)
注：连续型变量以(x±s)表示，用t检验比较变量的组间差异；分类变量以[n(%)]表示，变量间组间差异采用χ²检验。

下载: 导出CSV

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