基于医疗大数据开展药品上市后监测与评价研究的偏倚及其控制

doi:10.19803/j.1672-8629.2022.03.02

摘要/Abstract

摘要： 目的介绍国内外基于医疗大数据库开展上市后药品监测与评价研究的现状、存在的偏倚及其可能的控制方法,为我国药品风险监测与管理提供参考。方法系统总结欧美发达国家基于医疗大数据开展药品上市后监测与评价的具体实践,分析我国存在的差距;评价在研究设计、数据收集与标准化、统计分析等方面存在的问题及可以改进的方法;探讨我国目前面临的机遇与挑战。结果与结论 美国家已经基于医疗大数据建立了高效、可持续的药品上市后监测警戒系统;充分考虑并控制研究设计、实施和统计分析中的潜在偏倚是确定药品与不良反应因果关系的前提;应借鉴欧美先进经验,完善和整合现有医疗大数据,开展药物流行病学方法学研究。

关键词: 医疗大数据, 药品上市后, 监测, 选择偏倚, 信息偏倚, 混杂, 药物流行病学

Abstract: Objective To introduce the current situation, existing bias and possible control methods of post-marketing drug monitoring and evaluation research based on medical big databases at home and abroad, suggesting references for drug risk monitoring and management in China. Methods The specific practices of post-marketing drug monitoring and evaluation based on medical big data in the developed countries were systematically summarized, and the gaps in China were analyzed. The bias and control in research design, data collection, and statistical analysis were evaluated. In addition, the opportunities and challenges facing our country at present were assessed. Results and Conclusion The developed countries have established efficient and sustainable post-marketing monitoring and vigilance systems based on medical big data. Full consideration and control of potential bias in study design, implementation, and statistical analysis are necessary to determine the causal relationship between drugs and adverse reactions. We should learn from European and American advanced experience, improve and integrate existing medical big data, and carry out research on pharmacoepidemiology.

Key words: medical big data, post-marketing, monitoring, selection bias, information bias, confounding, pharmacoepidemiology

中图分类号:

R969.3

王丽, 王晓墨, 杨晨露. 基于医疗大数据开展药品上市后监测与评价研究的偏倚及其控制[J]. 中国药物警戒, 2022, 19(3): 239-243.

WANG Li, WANG Xiaomo, YANG Chenlu. Bias and control in the monitoring and evaluation of post-marketing drug using the medical big data[J]. Chinese Journal of Pharmacovigilance, 2022, 19(3): 239-243.

参考文献

[1] JICK H, WATKINS RN, HUNTER JR, et al.Replacement estrogens and endometrial cancer[J]. N Engl J Med, 1979, 300(5): 218-222.
[2] PLATT R, DAVIS R, FINKELSTEIN J, et al.Multicenter epidemiologic and health services research on therapeutics in the HMO research network center for education and research on therapeutics[J]. Pharmacoepidemiol Drug Saf, 2001, 10(5): 373-377.
[3] PLATT R, CARNAHAN RM, BROWN JS, et al.The U.S. Food and Drug Administration's mini-sentinel program: status and direction[J]. Pharmacoepidemiol Drug Saf, 2012, 21(Suppl 1): 1-8.
[4] HUANG TY, HOU L, ANDERSEN A, et al.Incidence of severe uterine bleeding outcomes among oral anticoagulant users and nonusers[J]. Am J Obstet Gynecol, 2021, 226(1):140-143.
[5] EWORUKE E, HOU L, ZHANG R, et al.Risk of severe abnormal uterine bleeding associated with rivaroxaban compared with apixaban, dabigatran and warfarin[J]. Drug Saf, 2021, 44(7): 753-763.
[6] EWORUKE E, HAUG N, BRADLEY M, et al. Risk of nonmelanoma skin cancer in association with use of hydrochlorothiazide-containing products in the United States[J]. JNCI Cancer Spectr, 2021, 5(2): pkab009.
[7] PLATT R, BROWN JS, ROBB M, et al.The FDA sentinel initiative - an evolving national resource[J]. N Engl J Med, 2018, 379(22): 2091-2093.
[8] EPSTEIN MM, DUTCHER SK, MARO JC, et al.Validation of an electronic algorithm for Hodgkin and non-Hodgkin lymphoma in ICD-10-CM[J]. Pharmacoepidemiol Drug Saf, 2021, 30(7): 910-917.
[9] LO RE V, 3rd, CARBONARI DM, JACOB J, et al. Validity of ICD-10-CM diagnoses to identify hospitalizations for serious infections among patients treated with biologic therapies[J]. Pharmacoepidemiol Drug Saf, 2021, 30(7): 899-909.
[10] ANDRADE SE, SHINDE M, MOORE SIMAS TA, et al.Validation of an ICD-10-based algorithm to identify stillbirth in the sentinel system[J]. Pharmacoepidemiol Drug Saf, 2021, 30(9): 1175-1183.
[11] GIBSON TB, NGUYEN MD, BURRELL T, et al.Electronic phenotyping of health outcomes of interest using a linked claims-electronic health record database: findings from a machine learning pilot project[J]. J Am Med Inform Assoc, 2021, 28(7): 1507-1517.
[12] WANG SV, MARO JC, GAGNE JJ, et al.A general propensity score for signal identification using tree-based scan statistics[J]. Am J Epidemiol, 2021, 190(7): 1424-1433.
[13] COLOMA PM, SCHUEMIE MJ, TRIFIRO G, et al.Combining electronic healthcare databases in Europe to allow for large-scale drug safety monitoring: the EU-ADR Project[J]. Pharmacoepidemiol Drug Saf, 2011, 20(1): 1-11.
[14] YEE A, MAJUMDAR SR, SIMPSON SH, et al.Statin use in type 2 diabetes mellitus is associated with a delay in starting insulin[J]. Diabet Med, 2004, 21(9): 962-967.
[15] LEVESQUE LE, HANLEY JA, KEZOUH A, et al.Problem of immortal time bias in cohort studies: example using statins for preventing progression of diabetes[J]. BMJ (Clinical research ed), 2010, 340: b5087.
[16] Observational Health Data Sciences and Informatics. OMOP Common Data Model[EB/OL]. (2020-03-25)[2021-11-08]. https://www.ohdsi.org/data-standardization/the-common-data-model/.
[17] LAMER A, DEPAS N, DOUTRELIGNE M, et al.Transforming French electronic health records into the observational medical outcome partnership's common data model: a feasibility study[J]. Appl Clin Inform, 2020, 11(1): E1.
[18] HABERSON A, RINNER C, GALL W.Standardizing Austrians claims data using the OMOP common data model: a feasibility study[J]. Stud Health Technol, 2019, 258: 151-152.
[19] BELENKAYA R, GURLEY MJ, GOLOZAR A, et al.Extending the OMOP common data model and standardized vocabularies to support observational cancer research[J]. JCO Clin Cancer Inform, 2021, 5: 12-20.
[20] FDA. Food and Drug Administration. Sentinel initiative[EB/OL]. (2020-11-08)[2021-11-20]. https://www.sentinelinitiative.org.
[21] WYNER Z, DUBLIN S, CHAMBERS C, et al.The FDA MyStudies app: a reusable platform for distributed clinical trials and real-world evidence studies[J]. JAMIA Open, 2020, 3(4): 500-505.
[22] SCHNEEWEISS S.Sensitivity analysis and external adjustment for unmeasured confounders in epidemiologic database studies of therapeutics[J]. Pharmacoepidemiol Drug Saf, 2006, 15(5): 291-303.
[23] VANDERWEELE TJ, DING P.Sensitivity analysis in observational research: introducing the E-value[J]. Ann Intern Med, 2017, 167(4): 268-274.
[24] ZHANG X, STAMEY JD, MATHUR MB.Assessing the impact of unmeasured confounders for credible and reliable real-world evidence[J]. Pharmacoepidemiol Drug Saf, 2020, 29(10): 1219-1227.
[25] SONG HB,SHEN CY.Thoughts on the application of electronic healthdata in the post-marketing study of drug safety[J]. China Food Drug Administration Magazine(中国食品药品监管), 2020, 11: 36-47.
[26] YANG Y, ZHOU X, GAO S, et al.Evaluation of electronic healthcare databases for post-marketing drug safety surveillance and pharmacoepidemiology in China[J]. Drug Saf, 2018, 41(1): 125-137.
[27] SHEN ZJ, XU JF, YE XF, et al.Current status of pharmacovigilance research based on administrative claims database[J]. Chinese Journal of Pharmacovigilance(中国药物警戒), 2019, 16(3):129-133.