Arlett, P., Kjaer, J., Broich, K., Cooke, E.: Real-world evidence in EU Medicines Regulation: Enabling Use and establishing value. Clin. Pharmacol. Ther. 111(1), 21–23 (2022)

Article  PubMed  Google Scholar 

Athey, S., Imbens, G.W.: Machine Learning Methods That Economists Should Know About. Annual Review of Economics 11(Volume 11, 2019): 685–725. (2019)

Belthangady, C., Stedden, W., Norgeot, B.: Minimizing bias in massive multi-arm observational studies with BCAUS: Balancing covariates automatically using supervision. BMC Med. Res. Methodol. 21(1), 190 (2021)

Article  PubMed  PubMed Central  Google Scholar 

Berger, M.L., Sox, H., Willke, R.J., Brixner, D.L., Eichler, H.G., Goettsch, W., Madigan, D., Makady, A., Schneeweiss, S., Tarricone, R., Wang, S.V., Watkins, J.: and C. Daniel Mullins. 2017. Good practices for real-world data studies of treatment and/or comparative effectiveness: Recommendations from the joint ISPOR-ISPE Special Task Force on real-world evidence in health care decision making. Pharmacoepidemiol Drug Saf. 26(9): 1033–1039

Brenner, H., Gefeller, O.: Use of the positive predictive value to correct for disease misclassification in epidemiologic studies. Am. J. Epidemiol. 138(11), 1007–1015 (1993)

Article  CAS  PubMed  Google Scholar 

Concato, J., Corrigan-Curay, J.: Real-world evidence - where are we now? N Engl. J. Med. 386(18), 1680–1682 (2022)

Article  CAS  PubMed  Google Scholar 

Concato, J., ElZarrad, M.: FDA Issues Draft Guidances on Real-World Evidence, Prepares to Publish More in Future [accessed on 2022]. (2022). https://www.fda.gov/drugs/news-events-human-drugs/fda-issues-draft-guidances-real-world-evidence-prepares-publish-more-future

Cox, E., Martin, B.C., Van Staa, T., Garbe, E., Siebert, U., Johnson, M.L.: Good research practices for comparative effectiveness research: Approaches to mitigate bias and confounding in the design of nonrandomized studies of treatment effects using secondary data sources: The International Society for Pharmacoeconomics and Outcomes Research Good Research Practices for Retrospective Database Analysis Task Force Report–Part II. Value Health. 12(8), 1053–1061 (2009)

Article  PubMed  Google Scholar 

Crump, R.K., Hotz, V.J., Imbens, G.W., Mitnik, O.A.: Dealing with limited overlap in estimation of average treatment effects. Biometrika. 96(1), 187–199 (2009)

Article  Google Scholar 

Danaei, G., Rodriguez, L.A., Cantero, O.F., Logan, R., Hernan, M.A.: Observational data for comparative effectiveness research: An emulation of randomised trials of statins and primary prevention of coronary heart disease. Stat. Methods Med. Res. 22(1), 70–96 (2013)

Article  PubMed  Google Scholar 

Dang, L.E., Gruber, S., Lee, H., Dahabreh, I.J., Stuart, E.A., Williamson, B.D., Wyss, R., Diaz, I., Ghosh, D., Kiciman, E., Alemayehu, D., Hoffman, K.L., Vossen, C.Y., Huml, R.A., Ravn, H., Kvist, K., Pratley, R., Shih, M.C., Pennello, G., Martin, D., Waddy, S.P., Barr, C.E., Akacha, M., Buse, J.B., van der Laan, M., Petersen, M.: A causal roadmap for generating high-quality real-world evidence. J. Clin. Transl Sci. 7(1), e212 (2023)

Article  PubMed  PubMed Central  Google Scholar 

Desai, R.J., Wang, S.V., Sreedhara, S.K., Zabotka, L., Khosrow-Khavar, F., Nelson, J.C., Shi, X., Toh, S., Wyss, R., Patorno, E., Dutcher, S., Li, J., Lee, H., Ball, R., Dal Pan, G., Segal, J.B., Suissa, S., Rothman, K.J., Greenland, S., Hernan, M.A., Heagerty, P.J., Schneeweiss, S.: Process guide for inferential studies using healthcare data from routine clinical practice to evaluate causal effects of drugs (PRINCIPLED): Considerations from the FDA Sentinel Innovation Center. BMJ. 384, e076460 (2024)

Article  PubMed  Google Scholar 

Digitale, J.C., Martin, J.N., Glymour, M.M.: Tutorial on directed acyclic graphs. J. Clin. Epidemiol. 142, 264–267 (2022)

Article  PubMed  Google Scholar 

Dondo, T.B., Hall, M., West, R.M., Jernberg, T., Lindahl, B., Bueno, H., Danchin, N., Deanfield, J.E., Hemingway, H., Fox, K.A.A., Timmis, A.D., Gale, C.P.: beta-blockers and Mortality after Acute myocardial infarction in patients without heart failure or ventricular dysfunction. J. Am. Coll. Cardiol. 69(22), 2710–2720 (2017)

Article  CAS  PubMed  PubMed Central  Google Scholar 

European Medicines Agency: ENCePP Guide on Methodological Standards in Pharmacoepidemiology [accessed on 2023]. (2023). https://www.encepp.eu/standards_and_guidances/methodologicalGuide.shtml

Ferguson, K.D., McCann, M., Katikireddi, S.V., Thomson, H., Green, M.J., Smith, D.J., Lewsey, J.D.: Evidence synthesis for constructing directed acyclic graphs (ESC-DAGs): A novel and systematic method for building directed acyclic graphs. Int. J. Epidemiol. 49(1), 322–329 (2020)

Article  PubMed  Google Scholar 

Flanagin, A., Lewis, R.J., Muth, C.C., Curfman, G.: What does the proposed causal inference Framework for Observational studies Mean for JAMA and the JAMA Network Journals? JAMA (2024)

U.S. Food and Drug Administration: Framework for FDA’s Real-World Evidence Program [accessed on 2018]. (2018). https://www.fda.gov/media/120060/download

Franklin, J.M., Schneeweiss, S.: When and how can Real World Data analyses substitute for randomized controlled trials? Clin. Pharmacol. Ther. 102(6), 924–933 (2017)

Article  PubMed  Google Scholar 

Gatto, N.M., Wang, S.V., Murk, W., Mattox, P., Brookhart, M.A., Bate, A., Schneeweiss, S., Rassen, J.A.: Visualizations throughout pharmacoepidemiology study planning, implementation, and reporting. Pharmacoepidemiol Drug Saf. 31(11), 1140–1152 (2022)

Article  PubMed  PubMed Central  Google Scholar 

Girman, C.J., Faries, D., Ryan, P., Rotelli, M., Belger, M., Binkowitz, B., O’Neill, R.: and C. E. R. S. W. G. Drug Information Association. 2014. Pre-study feasibility and identifying sensitivity analyses for protocol pre-specification in comparative effectiveness research. J. Comp. Eff. Res. 3(3): 259–270

Griffith, G.J., Morris, T.T., Tudball, M.J., Herbert, A., Mancano, G., Pike, L., Sharp, G.C., Sterne, J., Palmer, T.M., Davey Smith, G., Tilling, K., Zuccolo, L., Davies, N.M., Hemani, G.: Collider bias undermines our understanding of COVID-19 disease risk and severity. Nat. Commun. 11(1), 5749 (2020)

Article  CAS  PubMed  PubMed Central  Google Scholar 

Hernán, M.A.: The C-Word: Scientific euphemisms do not improve causal inference from Observational Data. Am. J. Public Health. 108(5), 616–619 (2018)

Article  PubMed  PubMed Central  Google Scholar 

Hernán, M.A., Robins, J.M.: Using Big Data to emulate a target Trial when a Randomized Trial is not available. Am. J. Epidemiol. 183(8), 758–764 (2016)

Article  PubMed  PubMed Central  Google Scholar 

Hernán, M., Robins, J.: Causal Inference: What if. Chapman & Hall/CRC, Boca Raton (2020)

Google Scholar 

International Society for Pharmacoeconomics and Outcomes Research (ISPOR): Strategic Initiatives: Real-World Evidence [accessed on 2022]. (2022). https://www.ispor.org/strategic-initiatives/real-world-evidence

International Society for Pharmacoepidemiology (ISPE): Position on Real-World Evidence [accessed on 2020]. (2020). https://pharmacoepi.org/pub/?id=136DECF1-C559-BA4F-92C4-CF6E3ED16BB6

Labrecque, J.A., Swanson, S.A.: Target trial emulation: Teaching epidemiology and beyond. Eur. J. Epidemiol. 32(6), 473–475 (2017)

Article  PubMed  PubMed Central  Google Scholar 

Lanes, S., Beachler, D.C.: Validation to correct for outcome misclassification bias. Pharmacoepidemiol Drug Saf. (2023)

Lash, T.L., Fox, M.P., Fink, A.K.: Applying Quantitative bias Analysis to Epidemiologic data. Springer (2009)

Lash, T.L., Fox, M.P., MacLehose, R.F., Maldonado, G., McCandless, L.C., Greenland, S.: Good practices for quantitative bias analysis. Int. J. Epidemiol. 43(6), 1969–1985 (2014)

Article  PubMed  Google Scholar 

Leahy, T.P., Kent, S., Sammon, C., Groenwold, R.H., Grieve, R., Ramagopalan, S., Gomes, M.: Unmeasured confounding in nonrandomized studies: Quantitative bias analysis in health technology assessment. J. Comp. Eff. Res. 11(12), 851–859 (2022)

Article  CAS  PubMed  Google Scholar 

Loveless, B.: A Complete Guide to Schema Theory and its Role in Education [accessed on 2022]. (2022). https://www.educationcorner.com/schema-theory/

Lund, J.L., Richardson, D.B., Sturmer, T.: The active comparator, new user study design in pharmacoepidemiology: Historical foundations and contemporary application. Curr. Epidemiol. Rep. 2(4), 221–228 (2015)

Article  PubMed  PubMed Central  Google Scholar 

Mai, X., Teng, C., Gao, Y., Governor, S., He, X., Kalloo, G., Hoffman, S., Mbiydzenyuy, D., Beachler, D.: A pragmatic comparison of logistic regression versus machine learning methods for propensity score estimation. Supplement: Abstracts of the 38th International Conference on Pharmacoepidemiology: Advancing Pharmacoepidemiology and Real-World Evidence for the Global Community, August 26–28, 2022, Copenhagen, Denmark. Pharmacoepidemiology and Drug Safety 31(S2). (2022)

Mullard, A.: 2021 FDA approvals. Nat. Rev. Drug Discov. 21(2), 83–88 (2022)

Article  CAS  PubMed  Google Scholar 

Onasanya, O., Hoffman, S., Harris, K., Dixon, R., Grabner, M.: Current applications of machine learning for causal inference in healthcare research using observational data. International Society for Pharmacoeconomics and Outcomes Research (ISPOR) Atlanta, GA. (2024)

Pearl, J.: Causal diagrams for empirical research. Biometrika. 82(4), 669–688 (1995)

Article  Google Scholar 

Prada-Ramallal, G., Takkouche, B., Figueiras, A.: Bias in pharmacoepidemiologic studies using secondary health care databases: A scoping review. BMC Med. Res. Methodol. 19(1), 53 (2019)

Article  PubMed  PubMed Central  Google Scholar 

Richardson, T.S., Robins, J.M.: Single World Intervention Graphs: A Primer [accessed on 2013]. (2013). https://www.stats.ox.ac.uk/~evans/uai13/Richardson.pdf

Richiardi, L., Bellocco, R., Zugna, D.: Mediation analysis in epidemiology: Methods, interpretation and bias. Int. J. Epidemiol. 42(5), 1511–1519 (2013)

Article  PubMed  Google Scholar 

Riis, A.H., Johansen, M.B., Jacobsen, J.B., Brookhart, M.A., Sturmer, T., Stovring, H.: Short look-back periods in pharmacoepidemiologic studies of new users of antibiotics and asthma medications introduce severe misclassification. Pharmacoepidemiol Drug Saf. 24(5), 478–485 (2015)

Article  PubMed  Google Scholar 

Rodrigues, D., Kreif, N., Lawrence-Jones, A., Barahona, M., Mayer, E.: Reflection on modern methods: Constructing directed acyclic graphs (DAGs) with domain experts for health services research. Int. J. Epidemiol. 51(4), 1339–1348 (2022)

Article  PubMed  PubMed Central