The Randomized Controlled Trial: The Gold Standard of Clinical Science and a Barrier to Innovation? - Tim Fayram, St. Jude Medical Inc.
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The Randomized Controlled Trial: The Gold Standard of Clinical Science and a Barrier to Innovation? - Tim Fayram, St. Jude Medical Inc.

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Tim Fayram, St. Jude Medical Inc. - Speaker at the marcus evans Medical Device R&D Summit Fall 2013, held in Palm Beach, FL delivered his presentation entitled The Randomized Controlled Trial: The ...

Tim Fayram, St. Jude Medical Inc. - Speaker at the marcus evans Medical Device R&D Summit Fall 2013, held in Palm Beach, FL delivered his presentation entitled The Randomized Controlled Trial: The Gold Standard of Clinical Science and a Barrier to Innovation?

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The Randomized Controlled Trial: The Gold Standard of Clinical Science and a Barrier to Innovation? - Tim Fayram, St. Jude Medical Inc. The Randomized Controlled Trial: The Gold Standard of Clinical Science and a Barrier to Innovation? - Tim Fayram, St. Jude Medical Inc. Presentation Transcript

  • The Randomized Controlled Trial is the Gold Standard for Clinical Evidence Is it a Barrier to Innovation? Tim Fayram MS Vice President of Research Sunnyvale, CA November 19, 2013
  • Introduction Who is Vinod Klosla? Entrepreneurial i E t i l icon i th Sili in the Silicon Valley Co founder Co-founder of Sun Microsystems and Daisy Systems Now leads Khosla Ventures Views on healthcare at a talk on the Stanford University Campus in June of 2012
  • Introduction Healthcare Innovation in the next 5 years … Approximately 500M people have access to healthcare Approximately 6B people don’t have access to healthcare and want access We face a rapidly increasing slope on the cost versus time graph How will the healthcare delivery y model adapt in the next 5 years?
  • Introduction Technology is our only hope … "Almost everything doctors know about medicine will be obsolete " obsolete, Most Physicians and Clinicians as we know them will be replaced by Computers and robotics for diagnosis and treatment Mobile devices will guide patients in the management of their disease states Big Data analysis of huge patient groups will lead to the development of new treatment strategies Innovation that has yet to happen I ti th t h tt h All of this will come at a much lower treatment cost per patient
  • The Healthcare Landscape: Is the focus on Outcomes and Comparative Effectiveness? Medical Device Epidemiology Network Initiative (MDEpiNet) 5
  • Healthcare Public Policy A dynamic environment Implementation of the Affordable Care Act A conservative regulatory approach seems to be subsiding A reimbursement approach that is asking for additional proof and is paying out less The rise of the Accountable Care Organization Comparative effectiveness research There are conflicting signals but the need for cost effectiveness evidence is stronger than ever before
  • Evidence New types of clinical evidence will be needed While scientifically and medically oriented evidence will always be a requirement Is the priority shifting from efficacy to economic efficiency? A new feature or product must demonstrate superiority over the existing standard of care f Pre-clinical evidence Per subject study costs are rivaling per patient study costs Translation issues from animal models to human models FDA’s new emphasis in computational modeling Probably has more of a role in academic research
  • A Question for Industry With the dynamic environment that we are currently in, can we continue to use the existing serial 1. 2. 3. 3 4. Research Development Clinical Regulatory approach that we have traditionally used when there is demand from new patients entering the system and an ever growing cost crisis in the existing healthcare delivery model?
  • The Randomized Controlled Trial (RCT) Recognized as the gold standard for conclusive evidence generation by scientists, clinicians, regulators, healthcare decision makers Requires at least two groups (treatment and control) with randomized assignments and crossovers Requires that the clinicians are blinded to remove bias and deliver objective results Requires rigorous statistical analysis for appropriate design and accurate analysis of results Requires significant funding, it takes a long time to enroll and follow It has inherent risks to patients, to clinics, to sponsors
  • The Randomized Controlled Trial (RCT) Challenge 1: Design Historical “soft” endpoints in many RCTs have led to a debate on the meaning of the results g Recent regulatory emphasis on increased power that the RCT can take years to enroll and complete Reliance on consistent site to site execution of protocols Challenge 2: Approval and Acceptance Results are subject peer review by independent panels Regulatory approval no longer guarantees reimbursement Providers are now being measured on how much they save and the public policy environment could change during an extended RCT Will the initial requirements in a large RCT be acceptable when it has been completed?
  • Are there alternatives to the RCT? A list of alternatives to explore 1. 2. 3. 4. 4 5. 6. 6 7. Smaller prospective feasibility research studies Larger observational outcomes studies Computational modeling as a substitute The use of “Big Data” analysis in public databases Big Data Mobile devices Patient social networks Automation
  • Prospective Feasibility Research Studies What are the opportunities? Smaller prospective studies (n < 100) take less time to enroll and complete Can still have a randomized treatment arm and a control arm with crossovers Patients can be followed for extended periods after the study is completed The di ti Th directional results can’t achieve statistical significance l lt ’t hi t ti ti l i ifi but those results can still have an impact More investigators are publishing on their early results
  • Observational Outcomes Studies What are the opportunities? Very large retrospective studies (n > 10000) take less time because the data already exists in registries and industry & government databases Observational and uncontrolled but very powerful The results are hypothesis generating Statistical significance is usually achieved Explanation for the result (Why?) may not be possible A commitment to publish the study results regardless of its outcome
  • Computational Modeling What are the opportunities? Computer simulations that can be used to model the biological interface Models contain a very large number of extremely small elements that virtually duplicate the actual interface Use of complex constraints and relations can differentiate between healthy and diseased models FDA h t k an i has taken increased i t d interest i thi methodology t in this th d l and spoken in support of it at various academic and industry conferences y Could save time in the product development cycle by virtually eliminating the need for pre-clinical studies on efficacy
  • “Big Data” Analytics How can technology address opportunities? Consider new sources for data that exist in very large de-identified patient groups In Cellular Networks In Government Networks In Social Networks Big Data analytics and algorithms could provide answers about the forest irrespective of the trees This concept is a new and growing opportunity
  • Mobile Devices How can technology address opportunities? Proliferation of healthcare smartphone apps Healthy d i k ti t have access t many apps H lth and sick patients h to This technology could provide a more consistent execution of clinician trials via mobile apps This technology could facilitate positive outcomes by enabling patient awareness Of their risk profiles Track their health progress Increase their compliance Provide an incentive to stay “healthy”
  • Patient Social Networks What are the opportunities? The world of social networking is becoming more popular in the older patient populations that are more likely to be sick It provides a unique opportunity to study patient behavior and compliance It could create patient groups that share information for the good of the group Connection of genotypes to phenotypes?
  • Automation in the Practice of Medicine How can automation address opportunities? Current diagnostic equipment is highly automated and acute minimally i t t d d t i i ll invasive i equipment is trending towards automation But not much else is More automation is on the horizon Automatic downloading of data with automatic analysis Patients enabling minimally invasive automatic treatments Leading to computers and robots that diagnose and treat patients in-clinic
  • The Question Is the RCT becoming a Barrier to Innovation? Our Goal in this environment should be to develop efficacious solutions that reduce treatment costs on a shorter time-to-market cycle This Healthcare Environment demands it
  • The Alternatives The answer may be found in parallel study strategies as a opposed to traditional and rigorous serial study strategies i l t d t t i 1. 1 2. 3. 4. 5. 6. 7. Smaller prospective feasibility research studies Larger observational outcomes studies Computational modeling as a substitute The use of “Big Data” analysis in public databases Mobile devices Patient social networks Automation
  • The Alternatives CardioMEMS: The CHAMPION Trial started in 2007 and has yet to be approved An alternative reality 1. Conduct a prospective feasibility research study for 6 months 2. Receive conditional limited regulatory approval 3. Continue to follow the patients from the feasibility study 4. Add a much larger pool of patients 5. Conduct a retrospective observational study for 1 year 6. Receive full regulatory approval in 2 years
  • Conclusions The worlds of healthcare and technology are on a common course Mobile apps, social networking and Big Data analytics are on exponential growth curves Automation algorithms will enable increased clinical accuracy with a shorter cycle time Medical computers and robotics could be morphing into robotic clinicians Publication of studies using this technology will be used as evidence by non-physician decision makers and stakeholders
  • Conclusions The worlds of healthcare and technology are on a common course The availability of new technology could be harnessed to shorten our clinical t i l h t li i l trials, enable better outcomes, save on th cost of treatment for patients that the t ft t tf ti t th t receive novel therapy
  • Conclusions The worlds of healthcare and technology are on a common course … The emphasis on economic efficiency may render the demand f proof of efficacy th d d for f f ffi Obsolete? Unnecessary? Irrelevant?
  • Conclusions Vinod Khosla bets big on big data By Michal Lev-Ram, writer November 13, 2013: 5:48 PM ET The Silicon Valley startup Ayasdi is just the beginning, the Sun Microsystems co-founder says. S Mi f d
  • "Almost everything doctors know about medicine will be obsolete," Khosla told the audience. Earlier this year, the long-time technologist and co-founder of Sun Microsystems led a $10 million funding round co founder in Ayasdi, a newish big data company that utilizes "topological analysis" to look for patterns in massive, often disparate data points. (In plain English, Ayasdi turns mountains of linear information, like what is found in health records, into geometric shapes that people can interact with.) The Palo Alto, Calif.-based company is one of an exploding number of big data start-ups. But while most companies' offerings rely on data scientists to query systems with questions questions, Ayasdi says its approach enables companies to glean insights they didn't know they were looking for. General Electric, Citi and Merck are among the company's early customers.