Efficacy, effectiveness, and comparative effectiveness in liver disease

The term ‘‘translational research’’ is commonly used to describe efforts made toward bridging the gap between discoveries made at ‘‘the bench’’ to the patient’s ‘‘bedside’’ by moving basic discoveries into a candidate health application such as the production of a new treatment or diagnostic test, which is typically assessed in clinical trials. However, the benefits of therapies and diagnostic tests observed in those studies are often reduced once they are implemented in clinical practice. Therefore, ‘‘efficacy’’ reflects the degree to which an intervention produces the expected result under carefully controlled conditions chosen to maximize the likelihood of observing an effect if it exists. By contrast, ‘‘effectiveness’’ addresses the extent to which an intervention is beneficial when deployed in medical practice settings and broader populations. It takes into account the benefits and harms of an intervention, and therefore can often be more relevant to health care decisions of providers and patients as well as policy evaluation. Efficacy is largely determined by the biological effects of a therapy, whereas effectiveness takes into account external factors such as individual patient characteristics, health system features, and societal influences. Given the possible chasm between efficacy and effectiveness, another frontier of translational research looms, which addresses the gap between clinical trials and the ‘‘real world’’ impact of individual-level or population-level interventions on health outcomes. The concept of effectiveness encompasses the impact of the healthcare system and human behavior. Figure 1 illustrates that in addition to pharmacological and physiological efficacy, other components must be available for an intervention to be effective. These include (1) availability and accessibility of the intervention to patients who can obtain benefit in appropriate health care settings, (2) identification of patients who are appropriate for the intervention, (3) recommendation of the intervention by providers, (4) acceptance of the intervention by patients, and (5) adherence to treatment at the recommended dosing for therapeutic coverage to fully achieve the benefits of therapy. Given the multiplicity of components involved with determining effectiveness, one could assess the interactions between these factors to come up with an estimate of how one particular intervention would perform in everyday practice. Models of effectiveness can be operationalized to evaluate therapeutic (e.g., treatment of viral hepatitis) or diagnostic (e.g., surveillance for hepatocellular carcinoma) measures in large populations. For example, in Table 1, under a scenario which assumes 50% efficacy for current hepatitis C therapy (therapy A) to achieve a sustained viral response in patients with genotype 1 infection, the overall effectiveness could be as low as 17% given relatively small decrements in the rates of access to care, accurate diagnostic testing, proper treatment recommendations by the providers, and ultimately acceptance/adherence to treatment by the patient. If a new therapy B is developed with increased efficacy (70%), the overall effectiveness of therapy B would be only slightly higher than therapy A (24%) if all other parameters remained unchanged. However, if the less efficacious therapy A becomes more widely available, its overall effectiveness might become greater than that of the more efficacious therapy B, based on greater confidence by providers and better acceptance and adherence by patients. These scenarios, however, are somewhat simplistic in the assumption the probabilities of access, recommendation, and adherence are independent of each other. It is likely, for example, that lack of access (e.g., among the underinsured or uninsured) would adversely influence the rates of diagnosis, recommendation, and adherence, thus leading to even lower effectiveness rate than shown in the example. As this example illustrates, the observed efficacy of treatments within clinical trials may not be easily replicated in the community. Therefore, it is also imperative to conduct studies for evaluating the effectiveness of interventions (Fig. 1). Initial observations of effectiveness stem from the documentation of wide variations in the use of Abbreviations: CER, comparative effectiveness research; IOM, Institute of Medicine; NIH, National Institutes of Health. Dr. El-Serag is Supported by grants from National Institutes of Health (NIH) (National Cancer Institute grant no. R01 125487, DK K24 04107), Dr. Kim is supported by a grant from the National Institutes of Health, (DK34238). Address reprint requests to: W. Ray Kim, M.D., M.B.A., Mayo Clinic (PL 6), 200 First Street, SW, Rochester, MN 55906. E-mail: kim.ray@mayo.edu; Fax: 507-538-3974. CopyrightVC 2010 by the American Association for the Study of Liver Diseases. Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/hep.23819 Potential conflict of interest: Dr. El-Serag received grants from Bayer and Schering-Plough. He is also a consultant for Vertex. Dr. Talwalkar received grants from Salix and Merck.