L. Etheredge
8/5/2012
Creating a High-Peformance CER Research System
Suggestions for PCORI
I. General The current clinical research system needs major upgrades and re-design so CER is done quickly, efficiently, and automatically.
2. Key Initiatives
-- Standard comparators for reporting results of clinical studies. With a light touch of HHS research management to create “standard comparators” for clinical studies, basic CER information would be available at time of market entry. To be required by FDA and in government-funded studies. Each new study could immediately be compared to previous studies. It is much more efficient to have CERneeded data collected and reported in original studies. Avoid AHRQ, 27,000
Cochrane collaborators, and PCORI doing inconclusive, multi-year CER metaanalyses of non-comparable studies. i
-- A new technology learning system. Develop CER registries/research questions/studies to learn as much as possible in the first 3 years after market entry, Expand Sentinel beyond safety to include CER. Apply to surgical procedures.
Use CED to incentivize.
-- A national network of pre-positioned, pre-designed, pre-populated, registries, patient-centered databases (Francis Collins’s proposals), and learning networks for CER. Enable rapid-learning from in silico research for all population subgroups and conditions, and reduce the need for time-consuming CER studies that spend years of valuable research time in collecting data. Fill key data gaps: a pediatric learning consortium, surgery, patients with multiple chronic conditions, rare diseases.
-- National “open science” databases for CER research. Make public the FDA’s
(de-identified) clinical databases – the world’s largest repository of clinical data http://www.fda.gov/AboutFDA/ReportsManualsForms/Reports/ucm274442.htm
– and add the other canonical studies that influence current clinical practices. Expand clinical trials.gov. Add future clinical studies funded by federal government or FDA required.
-- Work plans. Request HHS to develop, seek public comment on, and share with PCORI CER work plans that specify, for each priority area: (1) questions to be answered; (2) by when they will be answered; and (3) who is accountable for
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answering the question. Avoid 106-yr + history of inconclusive studies on prostate cancer surgery. Public comment can encourage discussion of new methods and ideas for how to find better answers to questions more quickly and accurately.
3. Methods and Means
-- Uses and limits of Patients Like Me databases: a paper considering the potential uses and limitations of “patients like me” databases, e.g. Francis Collins’s proposed 20-30M patient research network. Under what circumstances/for what conditions will it be feasible to answer CER questions simply by “looking up” the answers? How big an N is needed, what else needs to be considered?
-- Genetic variation in disease/responses to therapy for CER priority questions: a paper using dbGAP, NHGRI’s eMERGE, and the KP Biobank for a “first cut” on where CER research needs to be designed to consider genetic factors and suggest how that can be done.
-- Current variations and trends in clinical practice: a paper that uses HHS’s all-payer and other databases to identify the variations in clinical practices for similar populations and trends that could have patient payoffs from CER studies.
-- A “national research laboratories model” for CER research: a paper that considers whether a “national research laboratories” model, to complement individual-investigator-initiated RO1s, may be the most efficient way for carrying out a rapid-learning CER program, which will need to develop (and curate) large databases and implement a patient-centered research agenda. The DOE national labs model is a successful example.
-- Predictive model development as a CER initiative: a paper that develops the point of view that CER ultimately needs to produce predictive models for assessing individualized medical treatment options, at point of care, and suggests how a CER research program, rather than trying to answer every question by unique studies, might advance the CER field efficiently by identifying the studies needed to develop patient-focused predictive models. Reliable models for extrapolating RCT studies to older patients, to children, and to patients with multiple chronic conditions and taking multiple drugs would also be useful. Archimedes/ARCHeS/
IndiGO, et. al. i aka, among meta-analyzers, as "cumulative meta-analysis"
Lau, J., Schmid C.,Chalmers,T "Cumulative meta-analysis of clinical trials builds evidence for exemplary medical care" J. Clinical Epidemiology,1995;48(1):45-57
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