INDICATION

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ELIQUIS is indicated to reduce the risk of stroke and systemic embolism in patients with nonvalvular atrial fibrillation (NVAF).

ELIQUIS is indicated for the treatment of deep vein thrombosis (DVT) and pulmonary embolism (PE), and to reduce the risk of recurrent DVT and PE following initial therapy.

ELIQUIS is indicated for the prophylaxis of deep vein thrombosis (DVT), which may lead to pulmonary embolism (PE), in patients who have undergone hip or knee replacement surgery.

The Potential Role of
Real-World Evidence

Learn about the role that real-world effectiveness
and safety data may play in supporting RCTs when it comes to your decision making.

Real-World Evidence and Overview of
Select ELIQUIS Data Video

Dr. Steven Deitelzweig shares information about how real-world evidence may provide additional information about the effectiveness and safety associated with a product, in addition to randomized clinical trial information (or RCTs), to help inform clinical decisions.

Dr. Deitelzweig is a practicing physician, professor of medicine at the University of Queensland and Ochsner Clinical School, and a co-lead author on ARISTOPHANES, a retrospective, observational, pooled database analysis in patients with nonvalvular atrial fibrillation (NVAF).

Dr. Steven Deitelzweig shares information about how real-world evidence may provide additional information about the effectiveness and safety associated with a product, in addition to randomized clinical trial information (or RCTs), to help inform clinical decisions.
Dr. Deitelzweig is a practicing physician, professor of medicine at the University of Queensland and Ochsner Clinical School, and a co-lead author on ARISTOPHANES, a retrospective, observational, pooled database analysis in patients with nonvalvular atrial fibrillation (NVAF).

Please see U.S. FULL PRESCRIBING INFORMATION, including Boxed WARNINGS, and MEDICATION GUIDE.

RCTs=randomized controlled trials;
RWE=real-world evidence.

Frequently Asked
Questions

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  • Randomized controlled trials remain the gold standard for objectively evaluating treatment efficacy and safety of a product1 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.1 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 controlled2
  • 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 safety3

  • 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)1,4

  • There are inherent limitations to real-world analyses, including:
    • It is possible to have coding errors and missing data in the databases used5,6
    • There is an inability to determine whether the treatment was correctly prescribed or taken as prescribed6
    • Only association can be determined, and therefore causality cannot be determined3
    • Many real-world analyses are observational studies, and not all unobserved confounders may have been adjusted for or controlled5
    • Over-the-counter medication use and laboratory values are not typically captured in insurance claims data6
    • Results are applicable only to the population studied, and may not be generalizable to other populations7
  • Retrospective real-world analyses (ie, 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 administered8
  • Covariates (eg, age, gender, comorbidities) may have an effect on outcomes outside of the actual treatment effect9,10
  • Bias can affect treatment results if not accounted for in the analysis8

  • 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 treatment7,11
    • 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 patients7
      • 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 effect7

  • 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 confounding12

References

1. 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.

2. 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

3. 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

4. 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.

5. 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

6. 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

7. 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.

8. 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

9. 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

10. 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

11. 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.

12. 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.