The Power of Observation

Sep 01, 2011

Richard Gliklich, MD
As the name implies, in observational studies one 'observes.' The choice of therapy is determined by the patient and her physician rather than protocol, and, because participation does not impact which treatment a patient receives, these studies can enroll a greater diversity of patients. As such, these studies are generally viewed as more 'real-world' than classic clinical trials. As the importance of these studies have grown, so have efforts to develop best practice guidelines in the design, implementation, and evaluation of these studies, so that high-quality evidence can be used for decision-making among regulators, payers, physicians, and patients.

Real World Welcome

In recent years, regulators and payers have led the push toward observational studies. Regulators are demanding it as a requirement following approval. Their requests are mainly driven by the need to better understand the impact of the product in the real world, especially the safety profiles of medications and devices as they are actually used, in a broad range of patients. With the passing of the Food and Drug Administration Amendments Act (FDAAA) of 2007, the FDA was authorized to require post-marketing studies or clinical trials at the time of approval, or after approval if new safety information emerges. Before requiring a post-marketing clinical trial, however, FDA must first determine that a post-marketing study will not be sufficient to address the safety issue of concern. FDAAA also introduced Risk Evaluation and Mitigation Strategies (REMS) for monitoring risk post-approval. Elements to assure safe use (ETASU), a part of REMS, may require a registry in which every patient taking the medication must be included. FDAAA also introduced the Sentinel Initiative, a database system that actively gathers safety information on marketed products, and consists of different data sources, including EHR systems, claims databases, and patient registries. Sentinel represents the first major step in changing the FDA's approach to post-market surveillance, from a passive system to a more active one; observational data is a vital element of this new system.

In 2010, FDA asked the Institute of Medicine (IOM) to evaluate the scientific and ethical issues involved in conducting studies on the safety of approved drugs. The IOM committee was asked to identify the strengths and weaknesses of various approaches, including observational studies, patient registries, patient-level data meta-analyses, and randomized controlled trials (RCT). The IOM's July 2010 report found that an RCT should only be conducted to provide additional evidence if a "responsible policy decision cannot be made based either on the existing evidence or on evidence from new observational studies." The IOM findings demonstrate that observational studies are increasingly becoming recognized as a preferred tool to monitor products for real-world data on safety and effectiveness.

With the American Recovery and Reinvestment Act (ARRA) of 2009, regulators in the US also began to focus on comparative- and cost-effectiveness data, hoping that more effective medicines would both improve the quality of care, as well as reduce healthcare costs. ARRA launched comparative effectiveness research (CER) into the spotlight by setting aside $1 billion for conducting CER, including observational studies. In 2010, the Patient Protection and Affordable Care Act established the Patient-Centered Outcomes Research Institute (PCORI), an independent organization responsible for commissioning comparative effectiveness research, including observational studies. As more of these studies are being required to meet safety and effectiveness needs, the government has developed different initiatives to provide best practice guides and to monitor and track the studies themselves. One example of a government-sponsored initiative is the US Health and Human Services Agency for Healthcare Research and Quality (AHRQ) guide, "Registries for Evaluating Patient Outcomes: A User's Guide."

The European Medicines Agency (EMA) has conducted similar initiatives, like the European Network of Centres for Pharmacoepidemiology and Pharmacovigilance (ENCePP), a project aimed at strengthening post-market product monitoring. The project's goals include facilitating multicenter, independent, post-authorization safety studies (PASS) and studies focusing on a lack of efficacy. In 2010, ENCePP introduced the Code of Conduct for Independence and Transparency and the Checklist of Methodological Standards for ENCePP Study Protocols that were designed with an emphasis on non-interventional post-authorization studies.

The EMA also developed the PROTECT-EU project (Pharmacoepidemiological Research on Outcomes of Therapeutics by a European ConsorTium), a multinational collaboration of public, private, and academic organizations that together develop innovative methods in pharmacoepidemiology and pharmacovigilance. One of the objectives includes developing "methods for continuous benefit/risk monitoring of medicines, by integrating data on benefits and risks from clinical trials, observational studies, and spontaneous reports."

Like regulators, payers have also been demanding more real-world data for determining coverage. Many payers are already seeking observational data about the clinical and economic value of a product, as a prerequisite to a formulary position or a specific level of reimbursement. In some cases, this can create a catch-22 as payers seek real-world data before allowing the product to effectively gain access to the market. This quandary can be addressed to some extent using preapproval or data from other markets to perform modeling. When the question is not addressable with existing evidence, some entities will consider a limited coverage model while more evidence is accumulating. Both the US Centers for Medicare and Medicaid Services (CMS) and the UK National Institute for Health and Clinical Excellence (NICE) have policies based on this concept. Coverage 'with evidence development,' 'only in research,' and similar models in the private sector offer an opportunity to prove effectiveness or value over time, although they are used less frequently than one might imagine.

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