倾向性评分与孟德尔随机化国内研究现状

和思敏; 张雨; 彭刘庆; 景佳蕊; 陈淑婷; 王文杰; 高绒绒; 高雪; 高倩; 王彤

doi:10.16462/j.cnki.zhjbkz.2022.03.014

倾向性评分与孟德尔随机化国内研究现状

doi: 10.16462/j.cnki.zhjbkz.2022.03.014

030001 太原，山西医科大学公共卫生学院卫生统计学教研室

基金项目:

国家自然科学基金 81872715

国家自然科学基金 82073674

详细信息

通讯作者:
王彤，E-mail: tongwang@sxmu.edu.cn

中图分类号: R181.2
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- 被引次数: 0
出版历程
- 收稿日期: 2021-06-04
- 修回日期: 2021-08-25
- 网络出版日期: 2022-03-17
- 刊出日期: 2022-03-10

Research progress of propensity score and Mendelian randomization in China

Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan 030001, China

Funds:

National Natural Science Foundation of China 81872715

National Natural Science Foundation of China 82073674

More Information

Corresponding author: WANG Tong, E-mail: tongwang@sxmu.edu.cn

摘要

摘要: 混杂偏倚是非随机化研究中偏倚一类的重要来源，对混杂因素的控制是研究中获得可靠结果的保证。本文简要介绍了在非随机化研究中常用的两类混杂控制方法——倾向性评分和孟德尔随机化，并对近年来国内相关研究现状进行综述，为倾向性评分匹配和孟德尔随机化的应用提供建议。
- 倾向性评分 /
- 孟德尔随机化 /
- 混杂控制
Abstract: Confounding bias is a common source of bias in non-randomized studies, and the control of confounding factors is an important guarantee for reliable results. In this article, we briefly introduce two types of commonly used methods on confounding control: propensity scoring and Mendelian randomization. We reviewed the current research in recent years in China, with a view to provide recommendations for propensity score and Mendelian randomization research applications.
- Propensity score /
- Mendelian randomization /
- Confounding control

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参考文献(31)

[1]	Vandenbroucke JP. When are observational studies as credible as randomised trials?[J]. Lancet, 2004, 363(9422): 1728-1731. DOI: 10.1016/s0140-6736(4)16261-2.
[2]	黄丽红, 魏永越, 陈峰. 如何控制观察性疗效比较研究中的混杂因素: (一)已测量混杂因素的统计学分析方法[J]. 中华流行病学杂志, 2019, 40(10): 1304-1309. DOI: 10.3760/cma.j.issn.0254-6450.2019.10.024. Huang LH, Wei YY, Chen F. Confounder adjustment in observational comparative effectiveness researches: (1) statistical adjustment approaches for measured confounder[J]. Chin J Epidemiol, 2019, 40(10): 1304-1309. DOI: 10.3760/cma.j.issn.0254-6450.2019.10.024.
[3]	Rosenbaum PR, Rubin DB. The central role of the propensity score in observational studies for causal effects[J]. Biometrika, 1983, 70(1): 41-55. DOI: 10.1093/biomet/70.1.41.
[4]	陈峰, 于浩. 临床试验精选案例统计学解读[M]. 北京: 人民卫生出版社, 2015. Chen F, Yu H. Statistical interpretation of selected cases of clinical trials[M]. Beijing: People's Medical Publishing House, 2015.
[5]	An WH. Bayesian propensity score estimators: incorporating uncertainties in propensity scores into causal inference[J]. Sociol Methodol, 2010, 40(1): 151-189. DOI: 10.1111/j.1467-9531.2010.01226.x.
[6]	Kaplan D, Chen JS. A two-step Bayesian approach for propensity score analysis: simulations and case study[J]. Psychometrika, 2012, 77(3): 581-609. DOI: 10.1007/s11336-012-9262-8.
[7]	吴美京. 多组比较资料贝叶斯倾向性评分模型的构建及应用[D]. 上海: 第二军医大学, 2014. Wu MJ. Statistical modeling and application for multi-treatment Bayesian propensity score analysis[D]. Shanghai: Second Military Medical University, 2014.
[8]	于菲菲. 分类资料的多水平倾向性评分模型构建及应用[D]. 上海: 第二军医大学, 2016. Yu FF. The study and application of multilevel propensity score model of categorical data in the hierarchical structure data[D]. Shanghai: Second Military Medical University, 2016.
[9]	郭轶斌. 分类资料全局最优倾向性评分区间匹配的研究与应用[D]. 上海: 中国人民解放军海军军医大学, 2019. Guo YB. Research and application in global optimal propensity score interval matching for categorical data[D]. Shanghai: Naval Medical University, 2019.
[10]	谢申祥, 范鹏飞, 宛圆渊. 传统PSM-DID模型的改进与应用[J]. 统计研究, 2021, 38(2): 146-160. DOI: 10.19343/j.cnki.11-1302/c.2021.02.011. Xie SX, Fan PF, Wan YY. Improvement and application of classical PSM-DID model[J]. Stat Res, 2021, 38(2): 146-160. DOI: 10.19343/j.cnki.11-1302/c.2021.02.011.
[11]	郭威. 基于统计学习的逆概率加权方法研究及其在医学中的应用[D]. 上海: 中国人民解放军海军军医大学, 2018. Guo W. Research and application in medical studies of inverse probability weighting based on statistical learning[D]. Shanghai: Naval Medical University, 2018.
[12]	Gao Q, Zhang Y, Liang J, et al. High-dimensional generalized propensity score with application to omics data[J]. Brief Bioinform, 2021, 22(6): bbab331. DOI: 10.1093/bib/bbab331.
[13]	Flury BK, Riedwyl H. Standard distance in univariate and multivariate analysis[J]. Am Stat, 1986, 40(3): 249-251. DOI: 10.1080/00031305.1986.10475403.
[14]	黄福强, 许军, 安胜利. 多组间协变量均衡性评价方法的研究[J]. 中国卫生统计, 2018, 35(2): 172-176. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGWT201802003.htm Huang FQ, Xu J, An SL. A research on methods of balance evaluation among the covariates of multiple groups[J]. Chin J Heal Stat, 2018, 35(2): 172-176. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGWT201802003.htm
[15]	Dong J, Zhang JL, Zeng SX, et al. Subgroup balancing propensity score[J]. Stat Methods Med Res, 2020, 29(3): 659-676. DOI: 10.1177/0962280219870836.
[16]	Wang XY, Li L, Wang L, et al. Propensity score-adjusted three-component mixture model for drug-drug interaction data mining in FDA Adverse Event Reporting System[J]. Stat Med, 2020, 39(7): 996-1010. DOI: 10.1002/sim.8457.
[17]	Katan MB. Commentary: Mendelian randomization, 18 years on[J]. Int J Epidemiol, 2004, 33(1): 10-11. DOI: 10.1093/ije/dyh023.
[18]	Emdin CA, Khera AV, Kathiresan S. Mendelian randomization[J]. JAMA, 2017, 318(19): 1925-1926. DOI: 10.1001/jama.2017.17219.
[19]	Smith GD, Lawlor DA, Harbord R, et al. Clustered environments and randomized genes: a fundamental distinction between conventional and genetic epidemiology[J]. PLoS Med, 2007, 4(12): e352. DOI: 10.1371/journal.pmed.0040352.
[20]	Evans DM, Davey Smith G. Mendelian randomization: new applications in the coming age of hypothesis-free causality[J]. Annu Rev Genomics Hum Genet, 2015, 16: 327-350. DOI: 10.1146/annurev-genom-090314-050016.
[21]	Paternoster L, Tilling K, Davey Smith G. Genetic epidemiology and Mendelian randomization for informing disease therapeutics: Conceptual and methodological challenges[J]. PLoS Genet, 2017, 13(10): e1006944. DOI: 10.1371/journal.pgen.1006944.
[22]	高雪. 基于提高统计效能与校正多效性偏倚的改进孟德尔随机化模型构建及应用[D]. 太原: 山西医科大学, 2020. Gao X. The construction and application of improved Mendelian randomization model aiming at increasing statistical power and correcting for pleiotropic bias[D]. Taiyuan: Shanxi Medical University, 2020.
[23]	Sanderson E. Multivariable Mendelian randomization and mediation[J]. Cold Spring Harb Perspect Med, 2021, 11(2): a038984. DOI: 10.1101/cshperspect.a038984.
[24]	Zhao J, Ming JS, Hu XH, et al. Bayesian weighted Mendelian randomization for causal inference based on summary statistics[J]. Bioinformatics, 2020, 36(5): 1501-1508. DOI: 10.1093/bioinformatics/btz749.
[25]	Xu SQ, Fung WK, Liu ZH. MRCIP: a robust Mendelian randomization method accounting for correlated and idiosyncratic pleiotropy[J]. Brief Bioinform, 2021, 22(5): bbab019. DOI: 10.1093/bib/bbab019.
[26]	林丽娟. 基于方差—协方差结构校正的孟德尔随机化方法及其在因果关联研究中的应用[D]. 南京: 南京医科大学, 2019. Lin LJ. Mendelian randomization method modified by the variance-covariance structure and the application in causal association studies[D]. Nanjing: Nanjing Medical University, 2019.
[27]	Yuan ZS, Zhu HH, Zeng P, et al. Testing and controlling for horizontal pleiotropy with probabilistic Mendelian randomization in transcriptome-wide association studies[J]. Nat Commun, 2020, 11(1): 3861. DOI: 10.1038/s41467-020-17668-6.
[28]	Liu L, Zeng P, Xue FZ, et al. Multi-trait transcriptome-wide association studies with probabilistic Mendelian randomization[J]. Am J Hum Genet, 2021, 108(2): 240-256. DOI: 10.1016/j.ajhg.2020.12.006.
[29]	Fan QR, Zhang F, Wang WY, et al. GWAS summary-based pathway analysis correcting for the genetic confounding impact of environmental exposures[J]. Brief Bioinform, 2018, 19(5): 725-730. DOI: 10.1093/bib/bbx025.
[30]	Huang YT. Mendelian randomization using semiparametric linear transformation models[J]. Stat Med, 2020, 39(7): 890-905. DOI: 10.1002/sim.8449.
[31]	Li W, Jiang ZC, Geng Z, et al. Identification of causal effects with latent confounding and classical additive errors in treatment[J]. Biom J, 2018, 60(3): 498-515. DOI: 10.1002/bimj.201700048.