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

RW data - data used for decision making that are not collected in conventional randomized controlled trials (RCTs).

RW

Evidence (RWE) Data (RWD) - ISPOR Outcome (RWO)

RWD by ISPOR

The RWE value

• • RCT – can intervention work?

– Efficacy (experimental effectiveness) RW – how intervetion works in real world?

– Effectiveness (virtual-practical-every day) • Effectiveness < efficacy ?

SITS-MOST

The RCT value?

Minimisation of biases

•

selection bias

•

performance bias

•

detection bias

•

attrition bias

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RW – outcome assessment

• • • For patients who could not meet RCT inclusion and exclusion criteria In real world not driven by study protocol Against wide range of comparators

RW data - types

– Clinical: • Morbidity, mortality • Soft and hard end-point • Short and long term outcomes – Economic: • Medical and non-medical resource use • Unit costs – Patient Reported Outcomes (PRO – symptoms, functional status, HRQoL, treatment satisfaction, patients’ preference, compliance)

PRO in drug authorisation

• • US Department of Health and Human Services, Food and Drug Administration. Guidance for Industry Patient Reported Outcome Measures: Use in Medical Product Development to Support Labeling Claims. 2006. Available from: http://www.fda.gov/cber/gdlns/ prolbl.pdf

Szende A, Leidy NK, Revicki D. Health-related quality of life and other patient-reported outcomes in the European centralized drug regulatory process: a review of guidance documents and performed authorizations of medicinal products 1995 to 2003. Value Health 2005;8:534–48.

In reimbursement?

Sources of RW Data:

1) supplements to traditional registration RCTs 2) large simple trials (also called practical clinical trials)

3) registries or observational studies

4) administrative data 5) health surveys 6) electronic health records (EHRs) and medical chart reviews.

Registries

• • Reporting challenges Not to verify but rather to generate hypothesis Martin H. Prins – never use registry results to make statement on relative efficacy

of treatment option

• Selection bias huge

Registries 1. Disease- specific 2. Product/ health technology – specific 3. Focusing on services/ procedures

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Registries – types based on data source: •

Primary

•

Secondary

– Finish Stroke Registry 16

Acute coronary syndromes registry in Poland 17 Listopad 2012

Cancer registry in Poland

Listopad 2012 18

AIDS registry in Poland

Listopad 2012 19

Medical interventions Registry

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GRP – good registry practice

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Comparative effectiveness

GRACE: the conduct and synthesis of research comparing the benefits and harms of different interventions and strategies to prevent, diagnose, treat and monitor health conditions in ‘

real world

’ settings.

Liczne wytyczne: 2005 - Guidelines for good pharmacoepidemiologic practice 2007 - STROBE guidelines for reporting observational studies 2007 - AHRQ Guide for conducting comparative effectiveness reviews 2009 - ISPOR – Comparative Effectiveness Research Methods 2010 - AHRQ – Registries for Evaluating Patient Outcomes

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PROTOCOL

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SITS-MOST Protocol – content

• • • • • • • • • • • • Aims of the study Study design Treatment Study population Outcomes measure Investigational procedures Planned analyses Schedule of study procedures Patient identification and monitoring of source data Centre eligibility Administrative and ethical matters Study termination, confidentiality and publication policy 24

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

An ISPOR-AMCP-NPC Good Practice Task Force Report

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 a potential “fatal flaw” had been detected.

RWE benefit (1)

• • • • • Estimates of effectiveness rather than efficacy in a variety of typical practice settings; Comparison of multiple alternative interventions (e.g., older vs. newer drugs) or clinical strategies to inform optimal therapy choices beyond placebo comparators; Estimates of the evolving risk–benefit profile of a new intervention, including long-term (and rare) clinical benefits and harms; Examination of clinical outcomes in a diverse study population that reflects the range and distribution of patients observed in clinical practice; Results on a broader range of outcomes (e.g., PROs, HRQoL, and symptoms) than have traditionally been collected in RCTs (i.e., major morbidity and short-term mortality);

RWE benefit (2)

• • • • • • • Data on resource use for the costing of health-care services and economic evaluation; Information on how a product is dosed and applied in clinical practice and on levels of compliance and adherence to therapy Data in situations where it is not possible to conduct an RCT (e.g., narcotic abuse) Substantiation of data collected in more controlled settings Data in circumstances where there is an urgency to provide reimbursement for some therapies because it is the only therapy available and may be life-saving; Interim evidence—in the absence of RCT data—upon which preliminary decisions can be made Data on the net clinical, economic, and PRO impacts following implementation of coverage or payment policies or other health management programs (e.g., the kind of data CMS expects to collect under its coverage with evidence development policy)

RWE limitations

• • • For all nonrandomized data, the most significant concern is the potential for bias. Retrospective or prospective observational or database studies do not meet the methodological rigor of RCTs, despite the availability of sophisticated statistical approaches to adjust for selection bias in observational data: – Covariate adjustment, – propensity scores, – instrumental variables, etc. Observational studies need to be evaluated rigorously to identify sources of bias and confounding, and adjusted for these before estimating the impact of interventions on health outcomes. Observational or database studies may also require substantial resources.

RWE in reimbursement

Economic evaluation – cost per QALY Verification of previous decision – conditional

reimbursement with evidence development Decisions should not be “bureaucratically arbitrary”

RSS – risk sharing schemes = PBRSA

PBRSA

Key findings:

• •

Additional evidence collection is costly, and there are numerous barriers to establishing viable and cost-effective PBRSAs: negotiation, monitoring, and evaluation costs can be substantial.

Whether the cost of additional data collection is justified by the benefits of improved resource allocation decisions afforded by the additional evidence generated and the accompanying reduction in uncertainty.

Conclusions

• • •

Real-world data are essential for sound coverage, payment, and reimbursement decisions.

RCTs remain the gold standard for demonstrating clinical efficacy in restricted trial setting, but other designs—such as observational registries, claims databases, and practical clinical trials—can contribute to the evidence base needed for coverage and payment decisions.

It is critical that policymakers recognize the benefits, limitations, and methodological challenges in using RW data, and the need to carefully consider the costs and benefits of different forms of data collection in different situations.