Transcript Jak RW mogą wpłynąć na organizację i finansowanie opieki

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
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Acute coronary syndromes registry in Poland
Listopad 2012
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Cancer registry in Poland
Listopad 2012
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AIDS registry in Poland
Listopad 2012
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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
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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
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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.