This site is intended for U.S. formulary decision-makers

Frequently Asked Questions

See below for some common questions about both general real-world evidence and real-world evidence specific to ELIQUIS.

  • Examples of databases used in some select manufacturer-sponsored real-world effectiveness and safety analyses include the Centers for Medicare & Medicaid Services (CMS) database and the OptumLabs® Data Warehouse1,2*
    • These databases are large-scale, third-party databases, and they provide a diverse geographic representation of claims from health plans and government organizations2-4     

*Optum® is a registered trademark of Optum, Inc.

  • Baseline covariates in ELIQUIS manufacturer-sponsored real-world effectiveness and safety analyses are adjusted for using PS matching and/or IPTW2-7

IPTW=inverse probability treatment weighting; PS=propensity score.

  • Regression analysis is a common methodology used to describe the relationship between a set of independent variables (eg, the treatment being used) and a dependent variable (eg, clinical outcomes such as rate of bleeds or frequency of adverse events)8
  • A Cox proportional hazards regression model is a regression method that uses time-to-event data to generate hazard ratios by analyzing the association between a specified event and 1 or more predictor variables9,10

DVT/PE=deep vein thrombosis/pulmonary embolism.

  • Examples of outcomes from ELIQUIS real-world analyses include:
    • The rate of readmission for hospitalized NVAF patients treated with select oral anticoagulants who received a primary discharge diagnosis of:
    • stroke, including ischemic stroke, hemorrhagic stroke, and SE and/or major bleeding, including gastrointestinal bleeding, intracranial bleeding, and other types of major bleeding11
    • Healthcare costs among patients with NVAF who were treated with select oral anticoagulants, including cost associated with:
    • likelihood of all-cause hospitalization, hospitalization due to stroke/SE, hospitalization due to major bleeding–related conditions and healthcare costs, including all-cause healthcare, all-cause medical, all-cause pharmacy, all-cause hospitalization, all-cause emergency room/outpatient, stroke/SE-related medical, and major bleeding–related medical costs12

ELIQUIS increases the risk of bleeding and can cause serious, potentially fatal, bleeding.13

 

NVAF=nonvalvular atrial fibrillation; SE=systemic embolism.

  • Examples of peer-reviewed comparative effectiveness research guidance used in ELIQUIS manufacturer-sponsored real-world effectiveness and safety analyses include:
    • International Society for Pharmacoeconomics and Outcomes Research (ISPOR) Good Research Practices for comparative effectiveness research—Parts I, II, and III14-16
    • Journal of Managed Care & Specialty Pharmacy (JMCP) “Ten Commandments” for Conducting Comparative Effectiveness Research Using “Real-World Data”17

RWD=real-world data.

  • Randomized controlled trials remain the gold standard for objectively evaluating treatment efficacy and safety of a product18 and are also used to support US Food & Drug Administration (FDA) approval
  • There is a difference between treatment efficacy and treatment effectiveness. Treatment efficacy is a measurement of the short- or long-term efficacy and/or safety of a treatment versus a known comparator or placebo, using a carefully selected and narrowly defined homogeneous patient population.18 Treatment effectiveness refers to the impact associated with a treatment under usual or real-world treatment conditions in which patient, provider, and service system factors that can affect treatment outcomes are not controlled19
  • Real-world evidence may provide supplemental data. With this information, healthcare decision-makers may be able to better understand treatments or methods, or patterns of use and adherence. They may also investigate uncommon or unexpected safety signals or tolerability issues collected from routine clinical practice real-world analyses that evaluate effectiveness and safety20
  • Real-world economic analyses provide additional information on healthcare cost associated with medications from a number of databases across multiple patient populations5,11,12
  • Real-world data are commonly derived from electronic health records, medical claims and billing data, patient/disease registries, and patient-generated data (eg, patient surveys)18,21
  • There are inherent limitations to real-world analyses, including:
    • It is possible to have coding errors and missing data in the databases used14,22
    • There is an inability to determine whether the treatment was correctly prescribed or taken as prescribed14
    • Only association can be determined, and therefore causality cannot be determined20
    • Many real-world analyses are observational studies, and not all unobserved confounders may have been adjusted for or controlled22
    • Over-the-counter medication use and laboratory values are not typically captured in insurance claims data14
    • Results are applicable only to the population studied, and may not be generalizable to other populations23
  • Retrospective real-world analyses (i.e., database analyses) are subject to bias from the effect of baseline covariates, which are measured or observed variables that are associated with subjects before treatment is administered24
  • Covariates (e.g., age, gender, comorbidities) may have an effect on outcomes outside of the actual treatment effect1,25
  • Bias can affect treatment results if not accounted for in the analysis24

Examples of methods used to help reduce imbalances include:

  • Propensity score (PS) analyses
    • These seek to isolate the treatment as the only difference between treatment groups; 1:1 PS matching is a common statistical technique used to balance groups on baseline characteristics by assigning each subject in a group a PS based on the likelihood of treatment23,26
  • Inverse probability treatment weighting (IPTW)
    • IPTW is the inverse of the estimated PS for treated patients and the inverse of one minus the estimated PS for control patients23
    • IPTW results in estimates that are generalizable to the entire population from which the observed sample was taken. Patients who receive an unexpected treatment are weighted up to account for the many patients like them who did receive treatment. Patients who receive a typical treatment are weighted down because they are essentially over-represented in the data. These weights create a pseudo-population where the weighted treatment and control groups are representative of the patient characteristics in the overall population. The treatment effect obtained after applying IPTW is referred to as the population average treatment effect23
  • One approach researchers use is conducting sensitivity analyses, which are secondary analyses performed by modifying certain assumptions to test the robustness of the results and to assess the extent to which results may be due to unobserved confounding27

References

  1. Lip GYH, Keshishian A, Li X, et al. Effectiveness and safety of oral anticoagulants among nonvalvular atrial fibrillation patients [published correction appears in Stroke. 2020;51:e71. doi:10.0061/STR0000000000000227 and Stroke. 2020;51:e44. doi:10.00161/STR0000000000000218]. Stroke. 2018;49:2933-2944. doi:10.0061/STROKEAHA.118.020232
  2. Amin A, Keshishian A, Trocio J, et al. Risk of stroke/systemic embolism, major bleeding and associated costs in non-valvular atrial fibrillation patients who initiated apixaban, dabigatran or rivaroxaban compared with warfarin in the United States Medicare population. Curr Med Res Opin. 2017;33(9):1595-1604. doi:10.1080/03007995.2017.1345729
  3. About OptumLabs and UC Health Partnership. UC Davis Health Clinical and Translational Science Center. Accessed July 26, 2021. https://health.ucdavis.edu/ctsc/area/Resource_Library/optum_uchealth.html. Accessed July 26, 2021.
  4. Finding datasets for secondary analysis. Johns Hopkins University of Medicine. Updated July 26, 2021. https://browse.welch.jhmi.edu/datasets/medicare-data. Accessed July 26, 2021.
  5. Weycker D, Li X, Wygant GD, et al. Effectiveness and safety of apixaban versus warfarin as outpatient treatment of venous thromboembolism in U.S. clinical practice. J Thromb Haemost. 2018;118(11):1951-1961. doi:10.1055/s-0038-1673689
  6. Lip GYH, Keshishian AV, Kang AL, et al. Oral anticoagulants for nonvalvular atrial fibrillation in frail elderly patients: insights from the ARISTOPHANES study. J Intern Med. 2021;289:42-52. doi:10.1111/joim.13140
  7. Yao X, Abraham NS, Sangaralingham LR, et al. Effectiveness and safety of dabigatran, rivaroxaban, and apixaban versus warfarin in nonvalvular atrial fibrillation. J Am Heart Assoc. 2016;5(6):1-18. pii: e003725. doi:10.1161/JAHA.116.003725
  8. Anderson RP, Jin R, Grunkemeier GL. Understanding logistic regression analysis in clinical reports: an introduction. Ann Thorac Surg. 2003;75:753–757. doi:10.1016/s0003-4975(02)04683-0
  9. White SE. Glossary. Basic & Clinical Biostatistics. 5th ed. McGraw-Hill Education; 2020.
  10. StatsDirect. Cox (proportional hazards) regression. https://www.statsdirect.com/help/Default.htm#survival_analysis/cox_regression.htm. Accessed March 8, 2021.
  11. Deitelzweig S, Baker C, Dhamane AD, et al. Comparison of readmissions among hospitalized nonvalvular atrial fibrillation patients treated with oral anticoagulants in the United States. J Drug Assess. 2020;9(1):87-96. doi:10.1080/21556660.2020.1750418
  12. Amin A, Keshishian A, Trocio J, et al. A real-world observational study of hospitalization and health care costs among nonvalvular atrial fibrillation patients prescribed oral anticoagulants in the U.S. Medicare population. J Manag Care Spec Pharm. 2020;26(5):639-651. doi:10.18553/jmcp.2020.26.5.639-651
  13. ELIQUIS® (apixaban) Package Insert. Bristol-Myers Squibb Company, Princeton, NJ, and Pfizer Inc, New York, NY.
  14. Cox E, Martin BC, Van Staa T, Garbe E, Siebert U, Johnson ML. 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. 2009;12(8):1053-1061. doi:10.1111/j.1524-4733.2009.00601
  15. Berger ML, Mamdani M, Atkins D, Johnson ML. Good research practices for comparative effectiveness research: defining, reporting and interpreting nonrandomized studies of treatment effects using secondary data sources: The ISPOR Good Research Practices for Retrospective Database Analysis Task Force Report—Part I. Value Health. 2009;12(8):1044-1052. doi:10.1111/j.1524-4733.2009.00600.x. Epub 2009 Sep 29.
  16. Johnson ML, Crown W, Martin BC, Dormuth CR, Siebert U. 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(8):1062-1073.
  17. Willke RJ, Mullins CD. “Ten commandments” for conducting comparative effectiveness research using “real-world data.” J Manag Care Pharm. 2011;17(9 Suppl A):S10-S15.
  18. Katkade VB, Sanders KN, Zou KH. Real world data: an opportunity to supplement existing evidence for the use of long-established medicines in health care decision making. J Multidiscip Healthc. 2018;11:295-304.
  19. Nordon C, Karcher H, Groenwold RHH, et al; GetReal consortium. The "Efficacy-Effectiveness Gap": historical background and current conceptualization. Value Health. 2016;19(1):75-81. doi:10.1016/j.jval.2015.09.2938
  20. de Lusignan S, Crawford L, Munro N. Creating and using real-world evidence to answer questions about clinical effectiveness. J Innov Health Inform. 2015;22(3):368-373. doi:10.14236/jhi.v22i3.177
  21. Kalf R, Meinecke A-K. Healthcare databases with a focus on electronic health records. RWE Navigator website. https://rwe-navigator.eu/use-real-world-evidence/sources-of-real-world-data/healthcare-databases-with-a-focus-on-electronic-health-records. Accessed March 8, 2021.
  22. Brookhart MA, Stürmer T, Glynn RJ, Rassen J, Schneeweiss S. Confounding control in healthcare database research: challenges and potential approaches. Med Care. 2010;48(60):S114-S120. doi:10.1097/MLR.0b013e3181dbebe3
  23. Brookhart MA, Wyss R, Layton JB, Stürmer T. Propensity score methods for confounding control in nonexperimental research. Circ Cardiovasc Qual Outcomes. 2013;6:604-611. doi:10.1161/CIRCOUTCOMES.113.000359. Epub 2013 Sep 10.
  24. Committee for Proprietary Medicinal Products (CPMP). Committee for Proprietary Medicinal Products (CPMP): Points to consider on adjustment for baseline covariates. Stat Med. 2004;23(5):701-709. doi:10.1002/sim.1647
  25. Franklin JM, Glynn RJ, Martin D, Schneeweiss S. Evaluating the use of nonrandomized real-world data analyses for regulatory decision making. Clin Pharmacol Ther. 2019;105(4):867-877. doi:10.1002/cpt.1351
  26. Gant T, Crowland K. A practical guide to getting started with propensity scores. Paper 689-2017 presented at: SAS Global Forum 2017; April 2-5, 2017; Orlando, FL.
  27. Delaney JAC, Seeger JD. Sensitivity analysis. In: Velentas P, Dreyer NA, Nourjah P, et al, eds. Developing a Protocol for Observational Comparative Effectiveness Research: A User’s Guide. Agency for Healthcare Research and Quality (US); 2013.


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