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山东大学学报(医学版) ›› 2017, Vol. 55 ›› Issue (6): 42-46.doi: 10.6040/j.issn.1671-7554.0.2017.368

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原因别竞争风险模型及其在健康风险评估中的应用

王停停1,2,王金涛1,2,3,袁中尚1,2,苏萍1,2,薛付忠1,2   

  1. 1.山东大学公共卫生学院生物统计学系, 山东 济南 250012;2.山东大学齐鲁生物医学大数据研究中心, 山东 济南 250012;3.山东大学(威海)数学与统计学院统计学系, 山东 威海 264200
  • 收稿日期:2017-04-27 出版日期:2017-06-10 发布日期:2017-06-10
  • 通讯作者: 薛付忠. E-mail:xuefzh@sdu.edu.cn E-mail:xuefzh@sdu.edu.cn
  • 基金资助:
    山东大学青年学者未来计划建设项目(2016WLJH23)

Cause-specific hazard model and its applications in health risk assessment

WANG Tingting1,2, WANG Jintao1,2,3, YUAN Zhongshang1,2, SU Ping1,2, XUE Fuzhong1,2   

  1. 1. Department of Biostatistics, School of Public Health, Shandong University, Jinan 250012, Shandong, China;
    2. Cheeloo Research Center for Biomedical Big Data, Shandong University, Jinan 250012, Shandong, China;
    3. Department of Statistics, School of Mathematics and Statistics, Shandong University, Weihai 264200, Shandong, China
  • Received:2017-04-27 Online:2017-06-10 Published:2017-06-10

摘要: 目的 介绍原因别竞争风险模型原理及其在健康风险评估中的应用。 方法 围绕慢性病风险评估广泛存在竞争风险这一问题,对比传统的Cox模型,分别从建模原理、参数估计等方面介绍原因别竞争风险,进一步结合山东多中心出血性脑卒中病例随访队列,阐明原因别竞争风险模型的实际应用和效果。 结果 在竞争风险框架下,原因别风险模型针对不同的原因别结局分别建立Cox类(Cox-type)生存分析模型,在参数和累积风险函数估计上,沿用了经典的偏似然函数方法,保证了估计量的相合性和有效性,为精确计算绝对风险奠定了基础。依托山东多中心出血性脑卒中病例随访队列,将其用于脑卒中死亡结局风险评估,显示出良好的实用性。 结论 在健康风险评估中,当竞争风险不容忽视时,可选取原因别竞争风险模型消除竞争风险的影响,避免产生错误的结论。

关键词: 健康风险评估, 竞争风险, Cox模型, 原因别竞争风险模型

Abstract: Objective To introduce the theory of cause-specific hazard model and its applications in health risk assessment. Methods Given that competing risks are commonly encountered in health risk assessment, we have introduced the cause-specific hazard model from the perspective of modeling principle and parameter estimator, and further evaluate the efficiency and application based on the Shandong Multi-center cohort of hemorrhagic cerebral apoplexy. Results Under the framework of competing risk, the cause-specific hazard model constructed the cox-type survival model for each cause-specific endpoint. The parameter estimator can be obtained from the partial likelihood and thus have good properties, this can make sure that the absolute risk is accurate. Based on the Shandong Multi-center cohort of hemorrhagic cerebral apoplexy, it showed a good practicability in risk assessment of stroke death. Conclusion When competing risk can not be ignored, the cause-specific hazard model are preferred in health risk assessment.

Key words: Health risk assessment, Competing risks, Cox model, Cause-specific hazard model

中图分类号: 

  • R195
[1] Tomaselli, Gordon F. Prevention of cardiovascular disease and stroke: meeting the challenge[J]. JAMA, 2011, 306(19): 2147-2148.
[2] Pendlebury, Sarah T, Peter M. Prevalence, incidence, and factors associated with pre-stroke and post-stroke dementia: a systematic review and meta-analysis[J]. Lancet Neurol, 2009, 8(11): 1006-1018.
[3] Kinlay, Scott. Changes in stroke epidemiology, prevention, and treatment[J]. Circulation, 2011, 124(19): 494-496.
[4] Jia Q. Liu L, Wang Y. Risk factors and prevention of stroke in the Chinese population[J]. J Stroke Cerebrovasc Dis, 2011, 20(5): 395-400.
[5] Lengeler C, Armstrong-Schellenberg J, D'alessandro U, et al. Relative versus absolute risk of dying reduction after using insecticide-treated nets for malaria control in Africa[J]. Trop Med Int Health, 1998, 3(4): 286-290.
[6] Easton DF, Peto J, Babiker AG. Floating absolute risk: an alternative to relative risk in survival and case-control analysis avoiding an arbitrary reference group[J]. Stat Med, 1991, 10(7): 1025-1035.
[7] Halpern MT, Gillespie BW, Warner KE. Patterns of absolute risk of lung cancer mortality in former smokers[J]. J Natl Cancer Inst, 1993, 85(6): 457-464.
[8] Malenka DJ, Baron JA, Johansen S, et al. The framing effect of relative and absolute risk[J]. J Gen Intern Med, 1993, 8(10): 543-548.
[9] Plummer M. Improved estimates of floating absolute risk[J]. Stat Med, 2004, 23(1): 93-104.
[10] Seshadri S, Wolf PA. Lifetime risk of stroke and dementia: current concepts, and estimates from the Framingham Study[J]. Lancet Neurol, 2007, 6(12): 1106-1114.
[11] Gail MH. Personalized estimates of breast cancer risk in clinical practice and public health[J]. Stat Med, 2011, 30(10): 1090-1104.
[12] Prentice RL, Kalbfleisch JD, Peterson Jr AV, et al. The analysis of failure times in the presence of competing risks[J]. Biometrics, 1978, 34(4): 541-554.
[13] Gray RJ. A class of K-sample tests for comparing the cumulative incidence of a competing risk[J]. Ann Stat, 1988, 16(3):1141-1154.
[14] Cox DR. Regression models and life-tables[J]. J R Stat Soc, 1972, 34(2):187-220.
[15] Shen Q, Jin B, Min J, et al. Cox model and its application to prognostic analysis of malignant melanoma[J]. Journal of Nanjing Railway Medical College, 1987, 2(9):871-879.
[16] Gamel JW, McLean IW, Greenberg RA. Interval-by-interval cox model analysis of 3680 cases of intraocular melanoma shows a decline in the prognostic value of size and cell type over time after tumor excision[J]. Cancer, 1988, 61(3): 574-579.
[17] Therneau TM. Proceedings of the first Seattle symposium in biostatistics: survival analysis[J]. 1997, 1205(473): 51-84. doi:10.1007/978-1-4684-6316-3.
[18] Chevret S. Logistic or Cox model to identify risk factors of nosocomial infection: still a controversial issue[J]. Intensive Care Med, 2001, 27(10): 1559-1560.
[19] Guo X, Chen M, Ding L, et al. Application of Cox model in coagulation function in patients with primary liver cancer[J]. Hepatogastroenterology, 2010, 58(106): 326-330.
[20] Sengupta D. Review of modeling survival data: extending the Cox model[J]. Technometrics, 2012, 44(1):85-86.
[21] Venables WN, Ripley BD. Modern applied statistics with S-PLUS[M]. Dordrecht:Springer Science & Business Media, 2013.
[22] Therneau T. A package for survival analysis in S. version 2.38; 2015[CP/OL]. URL: http://cran. r-project. org/web/packages/survival/index. html[accessed 2016-06-17] [WebCite Cache ID 6iKkaCcfO] , 2016.
[23] Fine JP, Gray RJ. A proportional hazards model for the subdistribution of a competing risk[J]. JASA, 1999, 94(446): 496-509.
[24] Putter H, Fiocco M, Geskus RB. Tutorial in biostatistics: competing risks and multi-state models[J]. Stat Med, 2007, 26(11): 2389-2430.
[25] Ruan PK, Gray RJ. Analyses of cumulative incidence functions via non-parametric multiple imputation[J]. Stat Med, 2008, 27(27): 5709-5724.
[26] Allignol A, Beyersmann J. Software for fitting nonstandard proportional subdistribution hazards models[J]. Biostatistics, 2010, 11(4):674-675.
[27] De Wreede LC, Fiocco M, Putter H. The mstate package for estimation and prediction in non-and semi-parametric multi-state and competing risks models[J]. Comput Methods Programs Biomed, 2010, 99(3): 261-274.
[28] de Wreede LC, Fiocco M, Putter H. Mstate: an R package for the analysis of competing risks and multi-state models[J]. J Stat Softw, 2011, 38(7): 1-30.
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