[1] BLACK N.Why we need observational studies to evaluate the effectiveness of health care[J]. BMJ, 1996, 312(7040): 1215-1218. [2] LI M, SHI JP, YU HH.Relationship between the“Real World” research, randomized controlled trial and number of one randomized controlled trial in clinical therapeutic study[J]. Chinese Journal of Epidemiology(中华流行病学杂志), 2012, 33(3): 342-345. [3] Food and Drug Administration. Use of real world evidence to support regulatory decision marking for medical devices[EB/OL]. (2017-08-31)[2021-08-14]. https://www.fda.gov/media/99447/download. [4] National Medical Products Administration. Guidelines for drug development and review supported by real-world evidence (trial document)[EB/OL]. (2020-01-03)[2021-08-14]. https://www.nmpa.gov.cn/xxgk/ggtg/qtggtg/20200107151901190.html. [5] National Medical Products Administration. Real-world data guidelines for generating real-world evidence (Trial document)[EB/OL]. (2021-04-15)[2021-08-14] https://www.cde.org.cn/main/news/viewInfoCommon/2a1c437ed54e7b838a7e86f4ac21c539. [6] BOOTH CM, KARIM S, MACKILLOP WJ.Real-world data: towards achieving the achievable in cancer care[J]. Nat Rev Clin Oncol, 2019, 16(5): 312-325. [7] CHEN DF, LIU H.Methods and applications of medical big data mining(医学大数据挖掘方法与应用)[M]. Beijing: Peking University Medical Press, 2020. [8] MAKADY A, HAM RT, DE BOER A, et al.Policies for use of real-world data in health technology assessment (HTA): a comparative study of six HTA agencies[J]. Value Health, 2017, 20(4): 520-532. [9] KAHN MG, CALLAHAN TJ, BARNARD J, et al.A harmonized data quality assessment terminology and framework for the secondary use of electronic health record data[J]. EGEMS (Wash DC), 2016, 4(1): 1244. [10] Food and Drug Administration. Standardization and querying of data quality metrics and characteristics for electronic health data-data quality metrics system final report [EB/OL]. (2019-12-31) [ 2021-08-14]. https://dataquality.healthdatacollaboration.net/. [11] PEARL J, GLYMOUR M, JEWELL NP.Causal inference in statistics: a primer[M]. John Wiley, 2016. [12] HERNÁN MA, ROBINS JM. Causal inference: what if[M]. Boca Raton: Chapman & Hall/CRC, 2020. [13] QIU JP.Practical measurement methods for causal inference(因果推断实用计量的方法)[M]. Shanghai: Shanghai University of Finance and Economics Press, 2020. [14] HUANG LH, WEI YY, CHEN F.Confounder adjustment in observational comparative effectiveness researches II: statistical adjustment approaches for unmeasured confounders[J]. Chinese Journal of Epidemiology(中华流行病学杂志), 2019, 40(11): 1450-1455. [15] STERNE JA, HERNÁN MA, REEVES BC, et al. ROBINS-I: a tool for assessing risk of bias in non-randomised studies of interventions[J]. BMJ, 2016, 355: i4919. [16] SHEN HB.Epidemiology (Vol. 3)[流行病学(第3卷)][M]. Beijing: People's Medical Publishing House, 2014. [17] TANG JL, YANG ZY.Observation versus experiment, efficacy versus effectiveness[J]. Chinese Journal of Epidemiology(中华流行病学杂志), 2014, 35(3): 221-222. |