Роль и место данных реального мира (RWE) в реимберсменте

1. Роль и место данных реального мира (RWE) в реимберсменте Maciej Niewada, PhD, MD, MSc Department of Clinical Pharmacology Medical University of Warsaw Marcin Czech, PhD, MD, MBA Department of Pharmacoeconomics Medical University of Warsaw Business School, Warsaw University of Technology

2. RW data - data used for decisionmakingthat are not collected in conventional randomizedcontrolled trials (RCTs). Evidence (RWE) RW Data (RWD)- ISPOR Outcome (RWO)

3. RWD by ISPOR

4. The RWE value • RCT – caninterventionwork? • Efficacy (experimentaleffectiveness) • RW – howintervetionworks in real world? • Effectiveness (virtual-practical-everyday) • Effectiveness < efficacy ?

7. SITS-MOST

8. The RCT value?

9. Minimisation of biases • selectionbias • performance bias • detectionbias • attritionbias

10. RW – outcomeassessment • For patientswhocould not meet RCT inclusion and exclusioncriteria • In real world not driven by studyprotocol • Againstwiderange of comparators

11. RW data - types • Clinical: • Morbidity, mortality • Soft and hard end-point • Short and long term outcomes • Economic: • Medical and non-medicalresourceuse • Unit costs • PatientReported Outcomes (PRO – symptoms, functional status, HRQoL, treatmentsatisfaction, patients’ preference, compliance)

12. PRO in drugauthorisation • US Department of Health and Human Services, Foodand Drug Administration. Guidance for IndustryPatient Reported Outcome Measures: Use in MedicalProduct Development to Support Labeling Claims.2006. Available from: http://www.fda.gov/cber/gdlns/prolbl.pdf • Szende A, Leidy NK, Revicki D. Health-relatedquality of life and other patient-reported outcomes inthe European centralized drug regulatory process: areview of guidance documents and performed authorizationsof medicinal products 1995 to 2003. ValueHealth 2005;8:534–48. In reimbursement?

13. Sources of RW Data: 1) supplements to traditional registration RCTs 2)large simple trials (also called practical clinical trials) 3) registries orobservationalstudies 4) administrative data 5) health surveys 6) electronic health records (EHRs) and medical chart reviews.

14. Registries • Reporting challenges • Not to verify but rather to generatehypothesis • Selectionbiashuge Martin H. Prins – neveruse registry results to makestatement on relativeefficacy of treatmentoption

15. Registries • Disease- specific • Product/ health technology – specific • Focusing on services/ procedures

16. Registries – typesbased on data source: • Primary • Secondary • FinishStroke Registry

17. Acutecoronarysyndromes registry in Poland

18. Cancer registry in Poland

19. AIDS registry in Poland

20. Medicalinterventions Registry

21. GRP – good registry practice

22. Comparativeeffectiveness GRACE: the conduct andsynthesis of research comparing the benefits and harms of different interventions andstrategies to prevent, diagnose, treat and monitor health conditions in ‘real world’settings. Liczne wytyczne: 2005 - Guidelines for goodpharmacoepidemiologicpractice 2007 - STROBE guidelines for reportingobservationalstudies 2007 - AHRQ Guide for conductingcomparativeeffectivenessreviews 2009 - ISPOR – ComparativeEffectivenessResearchMethods 2010 - AHRQ – Registries for EvaluatingPatientOutcomes

23. PROTOCOL

24. SITS-MOST Protocol – content • Aims of the study • Study design • Treatment • Studypopulation • Outcomesmeasure • Investigationalprocedures • Plannedanalyses • Schedule of studyprocedures • Patientidentification and monitoring of source data • Centre eligibility • Administrative and ethicalmatters • Studytermination, confidentiality and publication policy

25. An ISPOR-AMCP-NPC Good Practice Task Force Reports 1) prospective 2) retrospective observational studies 3) network metaanalysis(indirect treatment comparison) 4) decision analyticmodeling studies with greater uniformity and transparency

26. An ISPOR-AMCP-NPC Good Practice Task Force Report

27. Summary flowchart for observational study assessment questionnaire. Red thumbs down icons indicate that a“weakness” had been detected in one of the elements that support credibility. Red skull and cross-bones icons indicate that apotential “fatal flaw” had been detected.

29. RWE benefit (1) • Estimates of effectiveness rather than efficacy in avariety of typical practice settings; • Comparison of multiple alternative interventions(e.g., older vs. newer drugs) or clinical strategies toinform optimal therapy choices beyond placebocomparators; • Estimates of the evolving risk–benefit profile of anew intervention, including long-term (and rare)clinical benefits and harms; • Examination of clinical outcomes in a diversestudy population that reflects the range and distributionof patients observed in clinical practice; • Results on a broader range of outcomes (e.g.,PROs, HRQoL, and symptoms) than have traditionallybeen collected in RCTs (i.e., major morbidityand short-term mortality);

30. RWE benefit (2) • Data on resource use for the costing of health-care services and economicevaluation; • Information on how a product is dosed andapplied in clinical practice and on levels of compliance and adherence to therapy • Data in situations where it is not possible toconduct an RCT (e.g., narcotic abuse) • Substantiation of data collected in more controlledsettings • Data in circumstances where there is an urgencyto provide reimbursement for some therapiesbecause it is the only therapy available and may be life-saving; • Interim evidence—in the absence of RCTdata—upon which preliminary decisions can bemade • Data on the net clinical, economic, and PROimpacts following implementation of coverage orpayment policies or other health managementprograms (e.g., the kind of data CMS expects tocollect under its coverage with evidence development policy)

31. The limitations of RW data?

32. RWE limitations • For all nonrandomized data, the most significantconcern is the potential for bias. • Retrospective or prospectiveobservational or database studies do not meetthe methodological rigor of RCTs, despite the availabilityof sophisticated statistical approaches to adjustfor selection bias in observational data: • Covariateadjustment, • propensityscores, • instrumentalvariables, etc. • Observational studies need to be evaluated rigorouslyto identify sources of bias and confounding,and adjusted for these before estimating the impactof interventions on health outcomes. Observationalor database studies may also require substantialresources.

33. RWE in reimbursement Economicevaluation – cost per QALY Verification of previousdecision – conditionalreimbursement with evidence development Decisions should not be “bureaucraticallyarbitrary” RSS – risksharingschemes = PBRSA

34. PBRSA

36. Keyfindings: • Additionalevidencecollectioniscostly, and therearenumerousbarriers to establishingviable and cost-effectivePBRSAs: negotiation, monitoring, and evaluationcostscan be substantial. • Whether the cost of additional data collectionisjustified by the benefitsof improvedresourceallocationdecisionsafforded by the additionalevidencegenerated and the accompanyingreduction in uncertainty.

39. Conclusions • Real-world data are essential for sound coverage,payment, and reimbursement decisions. • RCTs remain the gold standard for demonstratingclinical efficacy in restricted trial setting, but otherdesigns—such as observational registries, claims databases,and practical clinical trials—can contribute tothe evidence base needed for coverage and paymentdecisions. • It is critical that policymakers recognize the benefits,limitations, and methodological challenges in using RWdata, and the need to carefully consider the costs andbenefits of different forms of data collection in differentsituations.