Improving Care With Real-World Evidence
The potential disadvantages of retrospective studies that use RWD include the potential for selection bias and confounding because randomization is not possible.11 In randomized controlled trials, the random allocation is intended to balance known and unknown factors that can potentially influence the observed treatment effects. If not well-controlled through study design, then these known and unknown factors can result in biased or confounded study results. Furthermore, the use of RWD sources can increase the likelihood that there is missing data or that not all the desired information is collected as part of routine care. There are methodologies to help mitigate these challenges, including restriction of patients, selection of proper unexposed patients through direct matching or propensity score matching, instrumental variables, or proxy measures for variables of interest. Other mitigation strategies can be employed during analysis, including statistically controlling for confounding, stratification by covariates, or imputation methods.12
SUPPORTING REGULATORY DECISIONS
Real-world evidence studies based on RWD can support the regulatory decision-making process for medical devices and pharmaceuticals. In few and selected cases, such as rare or orphan diseases, studies including RWD have been considered in the pre-market approval process by regulatory authorities for new drug applications or line extensions.13 After market approval, studies based on RWD can add to the understanding of the safety and effectiveness of the device or drug when utilized under real-world circumstances.14
Some RWD sources, such as product registries, are specifically developed and designed for post-market device or drug surveillance purposes. In contrast, other RWD sources, such as EHRs and claims data, are usually established for non-regulatory purposes. Therefore, it is important to ensure that the RWE study based on secondary RWD sources fits the regulatory purpose and meets data quality and regulatory standards.15 In addition, there may be country-specific and product designation-specific differences in requirements and acceptance of RWE studies based on RWD in the context of the regulatory decision-making process.
RWE can expand the scientific understanding of the eff ectiveness and safety of interventions in individuals with ESKD such as determining the eff ectiveness of combining therapeutics (Figure 2). Despite the methodological challenges, secondary analyses of RWD can complement the existing evidence base and provide valuable insights that improve the care of individuals with kidney disease.
Meet The Experts
- Loudon K, Treweek S, Sullivan F, et al. The PRECIS-2 tool: designing trials that are fit for purpose. BMJ 2015;350:h2147. doi:10.1136/bmj.h2147.
- Stoll CRT, Izadi S, Fowler S, et al. Multimorbidity in randomized controlled trials of behavioral interventions: a systematic review. Health Psychol 2019;38(9):831-39. doi:10.1037/hea0000726.
- Kitchlu A, Shapiro J, Amir E, et al. Representation of patients with chronic kidney disease in trials of cancer therapy. JAMA 2018;319(23):2437-39. doi:10.1001/jama.2018.7260.
- Konstantinidis I, Patel S, Camargo M, et al. Representation and reporting of kidney disease in cerebrovascular disease: a systematic review of randomized controlled trials. PLoS One 2017;12(4):e0176145. doi:10.1371/journal.pone.0176145.
- O’Hare AM, Rodriguez RA, Bowling CB. Caring for patients with kidney disease: shifting the paradigm from evidence-based medicine to patient-centered care. Nephrol Dial Transplant 2016;31(3):368-75. doi:10.1093/ndt/gfv003.
- Sherman RE, Anderson SA, Dal Pan GJ, et al. Real-world evidence—what is it and what can it tell us? N Engl J Med 2016;375(23):2293-97. doi:10.1056/NEJMsb1609216.
- O’Hare AM, et al. Caring for patients with kidney disease.
- Orsini LS, Berger M, Crown W, et al. Improving transparency to build trust in real-world secondary data studies for hypothesis testing—why, what, and how: recommendations and a road map from the Real-World Evidence Transparency Initiative. Value Health 2020;23(9):1128-36. doi:10.1016/j.jval.2020.04.002.
- Chaudhuri S, Han H, Muchiutti C, et al. Remote treatment monitoring on hospitalization and technique failure rates in peritoneal dialysis patients. Kidney360 2020 March;1 (3):191-202. https://doi.org/10.34067/KID.0000302019.
- Brookhart MA, Sturmer T, Glynn RJ, et al. Confounding control in healthcare database research: challenges and potential approaches. Med Care 2010;48(6 Suppl):S114-20. doi:10.1097/MLR.0b013e3181dbebe3.
- Rothman K, Greenland S, Lash TL. Modern Epidemiology. 3rd ed. Philadelphia: Lippincott Williams & Wilkins, 2008.
- Bolislis WR, Fay M, Kühler TC. Use of real-world data for new drug applications and line extensions. Clin Ther 2020;42(5):926-38. doi: 10.1016/j. clinthera.2020.03.006.
- Brown JP, Wing K, Evans SJ, et al. Use of real-world evidence in postmarketing medicines regulation in the European Union: a systematic assessment of European Medicines Agency referrals 2013-2017. BMJ Open 2019;9(10):e028133. doi:10.1136/bmjopen-2018-028133.
- US Food and Drug Administration. Use of real-world evidence to support regulatory decision-making for medical devices: guidance for industry and Food and Drug Administration staff. August 2017. https://www.fda.gov/regulatory-information/search-fda-guidance-documents/use-real-world-evidence-support-regulatory-decision-making-medical-devices.