EJHP booklet for EAHP 2024

Original research

4 Wen W, Jing T, Yan R. Real-world data studies: update and future development. Chinese Journal of Evidence-based Medicine 2020;20. 5 Karanicolas PJ, Montori VM, Schünemann HJ, et al . ACP Journal Club. "Pragmatic" clinical trials: from whose perspective? Ann Intern Med 2009;150:Jc6-2–jc6-3.. 6 Kalkman S, van Thiel GJMW, Grobbee DE, et al . Pragmatic clinical trials: ethical imperatives and opportunities. Drug Discov Today 2018;23:1919–21. 7 Corrigan-Curay J, Sacks L, Woodcock J. Real-world evidence and real-world data for evaluating drug safety and effectiveness. JAMA 2018;320:867–8. 8 Brennan L, Watson M, Klaber R, et al . The importance of knowing context of hospital episode statistics when reconfiguring the NHS. BMJ 2012;344:e2432. 9 Cook JA, Collins GS. The rise of big clinical databases. Br J Surg 2015;102:e93–101. 10 Basch E, Schrag D. The evolving uses of "real-world" data. JAMA 2019;321:1359–60. 11 Ladha KS, Eikermann M. Codifying healthcare--big data and the issue of misclassification. BMC Anesthesiol 2015;15:179. 12 Neuman MD. The importance of validation studies in perioperative database research. Anesthesiology 2015;123:243–5. 13 Magnusson P, Palm A, Branden E, et al . Misclassification of hypertrophic cardiomyopathy: validation of diagnostic codes. Clin Epidemiol 2017;9:403–10. 14 Neubauer S, Kreis K, Klora M, et al . Access, use, and challenges of claims data analyses in Germany. Eur J Health Econ 2017;18:533–6. 15 U.S. Food & Drug Administration. Good pharmacovigilance practices and pharmacoepidemiologic assessment. Available: https://www.fda.gov/media/71546/ download 16 U.S. Food & Drug Administration. General Considerations for Clinical Studies E8(R1) (draft version). Available: https://www.fda.gov/media/82664/download [Accessed 8 May 2019]. 17 Epstein M, International Society of Pharmacoepidemiology. Guidelines for good pharmacoepidemiology practices (GPP). Pharmacoepidemiol Drug Saf 2005;14:589–95. 18 Wang W, Gao P, Wu J. Technical guidance for developing research databases using existing health and medical data [in Chinese]. Chinese Journal of Evidence-based Medicine 2019;19:763–70. 19 Prada-Ramallal G, Takkouche B, Figueiras A. Bias in pharmacoepidemiologic studies using secondary health care databases: a scoping review. BMC Med Res Methodol 2019;19:53. 20 Kyriacou DN, Lewis RJ. Confounding by indication in clinical research. JAMA 2016;316:1818–9. 21 Iudici M, Porcher R, Riveros C, et al . Time-dependent biases in observational studies of comparative effectiveness research in rheumatology. A methodological review. Ann Rheum Dis 2019;78:562–9. 22 Haneuse S. Distinguishing selection bias and confounding bias in comparative effectiveness research. Med Care 2016;54:e23–9. 23 Nohr EA, Liew Z. How to investigate and adjust for selection bias in cohort studies. Acta Obstet Gynecol Scand 2018;97:407–16. 24 Infante-Rivard C, Cusson A. Reflection on modern methods: selection bias-a review of recent developments. Int J Epidemiol 2018;47:1714–22. 25 U.S. Food & Drug Administration. Best practices for conducting and reporting pharmacoepidemiologic safety studies using electronic healthcare data. Available: https://www.fda.gov/media/79922/download [Accessed May 2013].

26 Lund JL, Richardson DB, Stürmer T. The active comparator, new user study design in pharmacoepidemiology: historical foundations and contemporary application. Curr Epidemiol Rep 2015;2:221–8. 27 Richesson RL, Sun J, Pathak J, et al . Clinical phenotyping in selected national networks: demonstrating the need for high-throughput, portable, and computational methods. Artif Intell Med 2016;71:57–61. 28 Groenwold RHH, Sterne JAC, Lawlor DA, et al . Sensitivity analysis for the effects of multiple unmeasured confounders. Ann Epidemiol 2016;26:605–11. 29 Suissa S. Immortal time bias in pharmaco-epidemiology. Am J Epidemiol 2008;167:492–9. 30 Haine D, Dohoo I, Dufour S. Selection and misclassification biases in longitudinal studies. Front Vet Sci 2018;5:99. 31 Zhang Z, Uddin MJ, Cheng J, et al . Instrumental variable analysis in the presence of unmeasured confounding. Ann Transl Med 2018;6:182. 32 Austin PC. An introduction to propensity score methods for reducing the effects of confounding in observational studies. Multivariate Behav Res 2011;46:399–424. 33 Wunsch H, Linde-Zwirble WT, Angus DC. Methods to adjust for bias and confounding in critical care health services research involving observational data. J Crit Care 2006;21:1–7. 34 Johnson ML, Crown W, Martin BC, et al . Good research practices for comparative effectiveness research: analytic methods to improve causal inference from nonrandomized studies of treatment effects using secondary data sources: the ISPOR Good Research Practices for Retrospective Database Analysis Task Force Report--part III. Value Health 2009;12:1062–73. 35 U.S. Food & Drug Administration. Best practices in drug and biological product postmarket safety surveillance for FDA staff (draft). Available: https://www.fda.gov/ media/130216/download [Accessed Nov 2019]. 36 Agency for Healthcare Research and Quality. AHRQ methods for effective health care. In: Velentgas P, Dreyer NA, Nourjah P, et al , eds. Developing a protocol for observational comparative effectiveness research: a user’s guide . Rockville, MD: Agency for Healthcare Research and Quality (US), 2013. 37 Public Policy Committee, International Society of Pharmacoepidemiology. Guidelines for good pharmacoepidemiology practice (GPP). Pharmacoepidemiol Drug Saf 2016;25:2–10. 38 Banack HR, Stokes A, Fox MP, et al . Stratified probabilistic bias analysis for body mass Index-related exposure misclassification in postmenopausal women. Epidemiology 2018;29:604–13. 39 Brown J, Kreiger N, Darlington GA, et al . Misclassification of exposure: coffee as a surrogate for caffeine intake. Am J Epidemiol 2001;153:815–20. 40 Hemkens LG, Benchimol EI, Langan SM, et al . The reporting of studies using routinely collected health data was often insufficient. J Clin Epidemiol 2016;79:104–11. 41 Nie X, Zhang Y, Wu Z, et al . Evaluation of reporting quality for observational studies using routinely collected health data in pharmacovigilance. Expert Opin Drug Saf 2018;17:661–8. 42 Cuschieri S. The STROBE guidelines. Saudi J Anaesth 2019;13:31–4. 43 Benchimol EI, Smeeth L, Guttmann A, et al . [The REporting of studies Conducted using Observational Routinely-collected health Data (RECORD) statement]. Z Evid Fortbild Qual Gesundhwes 2016;115-116:33–48. 44 Langan SM, Schmidt SA, Wing K, et al . The reporting of studies conducted using observational routinely collected health data statement for pharmacoepidemiology (RECORD-PE). BMJ 2018;363:k3532.

Liu M, et al . Eur J Hosp Pharm 2022; 29 :8–11. doi:10.1136/ejhpharm-2021-003081

11

16

BMJ1564-EAHP-Article-2.indd 16

26/02/2024 10:12

Powered by