Comparative effectiveness research

From Wikipedia, the free encyclopedia
Jump to: navigation, search

Comparative effectiveness research (CER) is the direct comparison of existing health care interventions to determine which work best for which patients and which pose the greatest benefits and harms. The core question of comparative effectiveness research is which treatment works best, for whom, and under what circumstances.

The Institute of Medicine committee has defined CER as "the generation and synthesis of evidence that compares the benefits and harms of alternative methods to prevent, diagnose, treat, and monitor a clinical condition or to improve the delivery of care. The purpose of CER is to assist consumers, clinicians, purchasers, and policy makers to make informed decisions that will improve health care at both the individual and population levels." [1]

An important component of CER is the concept of Pragmatic Trials.[2] These clinical research trials measure effectiveness—the benefit the treatment produces in routine clinical practice. This is different from regular clinical trials, which measure efficacy, whether the treatment works or not.

Dr.John Wennberg and his colleagues at the Dartmouth Institute for Health Policy and Clinical Practice have spent over 40 years documenting geographic variation in health care that patients in the U.S. receive - a phenomenon called practice pattern variation.

The Dartmouth researchers concluded that if unwarranted variation were eliminated, the quality of care would increase and health care savings up to 30% would be possible [3] - a statistic that has been often repeated in the case for CER.

Several groups have emerged to provide leadership in the area of Comparative Effectiveness Research. The Agency for Healthcare Research and Quality (AHRQ) is a federal agency focused on health care quality. The Institute for Clinical and Economic Review provides independent evaluation of the clinical effectiveness and comparative value of health care interventions, while also overseeing the New England Comparative Effectiveness Public Advisory Council (CEPAC), an independent body of physicians and patient representatives that aids patients, physicians and policymakers in the application and use of comparative effectiveness information to improve the quality and value of healthcare in the region.

In the 2010 U.S. health care reform[edit]

The rising cost of medical care in the U.S. has triggered an immediate need for better value in our health system. Researchers at the Dartmouth Institute for Health Policy, in addition to the Congressional Budget Office, have documented a large gap in the quality and outcomes and health services being delivered. Unwarranted variation in medical treatment, cost, and outcomes suggests a substantial area for improvement and savings in our health care system. Statistical findings show that "patients in the highest-spending regions of the country receive 60 percent more health services than those in the lowest-spending regions, yet this additional care is not associated with improved outcomes." [4] New models of shared decision making promise to bring greater emphasis to informed patient choice for "preference-sensitive" care, improving quality, safety, and effectiveness of health care by providing both patients and their health care providers with the evidence to assist in informed decision making.[5]

In 2009, $1.1 billion of President Barack Obama's stimulus package was earmarked for CER.[6] There was initial disagreement regarding whether CER will be used to limit patient health care options,[7] or help lower health care costs.[8] Ultimately the bill approved by Senate contains measures to utilize CER as a means for increasing quality while reducing rising costs.[9][10][11]

Incremental cost-effectiveness ratio[edit]

The Incremental cost-effectiveness ratio (ICER) is used to assess the merit of resource allocation for societal projects (such as healthcare), by calculating the additional cost of extending a particular (health) intervention divided by the additional (health) gain that would result as part of a cost-effective analysis (CEA). CEA is presented in the research literature as a methodology to help decision-makers allocate scarce resources. The analytical tool of CEA is the ICER given by the difference in costs between two health programs divided by the difference in outcomes between the programs. The comparison is typically between a new healthcare program and the existing approach to dealing with the same patient group. As a metric for cost-effectiveness studies, the ICER provides the results in Quality-Adjusted Life Years QALYs gained or lost for use in deciding whether a new program should be adopted or rejected. Individual programs can also be judged in terms of the absolute value of the ICER. Programs with ICERs that lie below a “threshold” ICER, also referred to as the lambda,[1] are deemed to be cost effective and should be adopted because the ”price” for producing health improvements implied by the ICER is acceptable.

Quality-Adjusted Life Year[edit]

The Quality-Adjusted Life Year (QALY) is a unit for measuring the health gain of an intervention calculated as the number of years of life saved and adjusted for quality generally applied to the denominator in the ICER2. The quality-adjusted life year (QALY) is a measure of disease burden, including both the quality and the quantity of life lived. It is used in assessing the value for money of a medical intervention. The QALY model requires utility independent, risk neutral, and constant proportional tradeoff behavior. The QALY is often used in cost-utility analysis to calculate the ratio of cost to QALYs saved for a particular health care intervention. This is then used to allocate healthcare resources, with an intervention with a lower cost to QALY saved ratio being preferred over an intervention with a higher ratio. The meaning and usefulness of the QALY is debated, however. The QALY is based on the number of years of life that would be added by the intervention. Each year in perfect health is assigned the value of 1.0 down to a value of 0.0 for death. If the extra years would not be lived in peak health, for example if the patient would lose a limb, or be blind or have to use a wheelchair, then the extra life-years are downgraded to a value less than 1 (but above 0) to account for this. The QALY is a measure of the value of health outcomes. Since health is a function of length of life and quality of life, the QALY was developed as an attempt to combine the value of these attributes into a single index number. The basic idea underlying the QALY is simple: it assumes that a year of life lived in perfect health is worth 1 QALY (1 Year of Life × 1 Utility value = 1 QALY) and that a year of life lived in a state of less than this perfect health is worth less than 1. In order to determine the exact QALY value, it is sufficient to multiply the utility value associated with a given state of health by the years lived in that state. QALYs are therefore expressed in terms of "years lived in perfect health": half a year lived in perfect health is equivalent to 0.5 QALYs (0.5 years × 1 Utility), the same as 1 year of life lived in a situation with utility 0.5 (e.g. bedridden) (1 year × 0.5 Utility). QALYs can then be incorporated with medical costs to arrive at a final common denominator of cost/QALY. This parameter can be used to develop a cost-effectiveness analysis of any treatment.[2]

ICER use in business or interventions[edit]

An ICER is an economic measurement used to study business or therapeutic interventional alternatives which have both separate costs and separate outcomes.3 Doing this requires taking the difference between the two costs and dividing it by the difference in the two outcomes, thereby showing how much an alternative will cost to effect one unit of difference in the eventual outcome. Most commonly, an incremental cost effectiveness ratio, or ICER, is used to decide between alternative treatments in the medical field. Although it is difficult to measure the outcome of medical procedures in terms of tangible results to plug into the equation, medical economic professionals have methods to determine ICERs with some degree of accuracy.4 Cost-effectiveness analysis helps identify ways to redirect resources to achieve more. It demonstrates not only the utility of allocating resources from ineffective to effective interventions, but also the utility of allocating resources from less to more cost-effective interventions.

Cost-effectiveness plane or matrix thresholds[edit]

The cost-effectiveness plane or matrix graphically depicts ICER-based merit of assessed projects plotted into four quadrants (increased cost/decreased benefit, increased cost/increased benefit, decreased cost/decreased benefit, and decreased cost/increased benefit). This graphical depiction thus stratifies the meaning of the ICER into four meaningful outcomes.5 Furthermore, circumstances in which cost and effectiveness are equally lowered or increased yielding identical ICER values are mapped according to cost and effectiveness coordinates, providing the basis for the ICER numeric value. Points in this Bayesian coordinate scheme are plotted along the x-axis according to the value ε1 − ε0, where ε1 is the experimental effectiveness and ε0 is the control effectiveness—or difference in effectiveness.6 Points are plotted along the y-axis γ1 − γ0, where γ1 is the experimental cost and γ0 is the control cost—or difference in cost. The result is a logical framework in which each quadrant represents an experimental outcome compared with the mean. Quadrants are numbered I-IV (see figure 1) and each connotes a different cost/effectiveness outcome with respect to the control. New interventions typically plot into in quadrant I with higher cost and effectiveness compared with established treatments. Experimental treatments in quadrant II cost more and decrease effectiveness compared with the control, while treatments in quadrant III decrease both cost and effectiveness and treatments in quadrant IV decrease cost and increase effectiveness.

This system has obvious benefits when the potential for ambiguity in ICER is considered. For example, if an experimental treatment increases costs by 10 units and effectiveness by 5 units—plotting into quadrant I—and another treatment decreases costs by 10 units and decreases effectiveness by 5 units—plotting into quadrant III—the ICER is 2 for both treatments, despite their contradictory attributes. While this tetralogical framework simplifies the significance of the ICER, there is proportional significance to the points within the Cartesian system. For example, lower ICER values in quadrant I denote greater benefit per unit cost, approaching quadrant IV spatially and in terms of value. Likewise, higher ICER values in quadrant III approach quadrant IV and boast greater savings per unit of effectiveness sacrificed.

ICER Bayesian Tetralogical Matrix.jpg

Government use of the ICER and CEA[edit]

The ICER is employed in government committees to estimate societal value of proposed projects (such as the National Institute for Health and Clinical Excellence (NICE) in the United Kingdom (UK): the extra-welfarist approach. Many nations rely on cost effectiveness to allocate public funds for health services and products. Most notably, in the UK, the NICE applies decision rules to ICERs in judging the economic merit of new technological innovations. Empirical analysis suggests that NICE sets a threshold cost per quality adjusted life year gained in the £20,000-£30,000 range.7 However, formal ICER thresholding is denied by NICE officials, although it is clearly considered in their analysis of treatment benefit.

While discrete ICER thresholds are generally avoided because of the political distastefulness, ICER values clearly impact decision-making. For example, NICE findings generally correlate with ICER values. In other words, accepted therapies tend to have lower ICER values. NICE-accepted treatments include: smoking cessation (ICER £430), asthma inhalers (ICER £5000) and first-line non-small cell lung cancer treatment (ICER £9475). Sample rejected treatments include: advanced colorectal cancer treatment (ICER £29,000), laparoscopic hernia repair (ICER £50,000) and beta interferon (ICER £187,000).8

While it is instructive to consider the impact of ICER’s in setting healthcare policy, other issues factor into the decision-making process. For example, other factors considered by NICE include: the uncertainty of cost effectiveness, the net cost to the National Health Service (NHS), the burden of disease, the availability of other treatments and others.

The Patient Protection and Affordable Care Act[edit]

The Patient-Centered Outcomes Research Institute (PCORI) was established to conduct comparative effectiveness research but the Patient Protection and Affordable Care Act (PPACA) prohibits it from using cost per QALY ICER thresholding9 (the US is characteristically welfarist in its attitude). The PPACA states:

The Patient-Centered Outcomes Research Institute…shall not develop or employ a dollars per quality adjusted life year (or similar measure that discounts the value of a life because of an individual’s disability) as a threshold to establish what type of health care is cost effective or recommended.

Indeed, PCORI Executive Director, Joe Selby said, “You can take it to the bank that PCORI will never do a cost-effectiveness analysis.”11 Whereas other nations refer to human metrics, such as QALYs, in assessing the merit and financial viability of embracing new healthcare technology and innovations (extra-welfarist), the U.S. typically avoids this stance (welfarist). However, while the notion of setting specific thresholds is prohibited, incorporating ICER into the decision-making process has not been expressly banned by the PPACA. Fears surrounding the potential for discrimination based on age and disability—as the wording of the PPACA attests—form the basis of the aversion to relying on ICER and cost effectiveness. The use of ICER favoring the young and healthy conjures images of “death panels” and “big government” stirring fear and mistrust in the minds of independently-minded Americans.

CEA (extra welfarist) versus CBA (welfarist)[edit]

Implementation of the ICER and CEA meets resistance and sparks controversy and debate between proponents of the welfarist viewpoint and their extra-welfarist counterparts. Welfarism upholds the notion that utility-driven individuals are the best suited to promoted their own welfare, which follows the Pareto paradigm that if one individual improves his/her station with none adversely affected, welfare is globally enhanced.12 As such, welfarists view healthcare output as the extent to which overall welfare is augmented—the sum of all individual utilities. In this scheme, welfare is measured in terms of individual preferences for health outcomes including, and other than, health.

In contrast, the extra-welfarists (sometimes called non-welfarists) targets health, rather than welfare and the sum of utilities, as their goal. Extra-welfarists negate individual differences in adaptation or expectation related to coping with disease.13 They assume that healthcare inputs equally benefit all individuals, in contradistinction to the welfarist perspective. This viewpoint eliminates the individual freedom to decide to pursue or eschew healthcare inputs based on unique, individual attributes upon which welfarism is based.14

The controversy flares when the issue of investigating healthcare interventions arises. Generic utility analysis studies conform to the cost benefit analysis (CBA) model whereby all costs and outcomes are monetized (converted to economic terms). However, the notion of converting health into economic terms is widely repugnant and difficult to implement on a wide scale basis. Cost effectiveness analysis (CEA) substitutes monetary figures with health equivalents, eliminating the dehumanizing element of equating health with economic metrics.

QALYs replaces economic measures in CEA. As previously discussed, QALY is an estimate of the life value of dollars spent on medical interventions. While this ostensibly offends the welfarist perspective because of the philosophical attachment to the individual primacy in determining utility, practically and politically, two major groups most vocally reject this approach in the U.S. On the one had, opposition arises from stakeholders with an interest in squelching CEA studies—pharmaceutical and medical device firms and procedurally-oriented physicians who benefit from unchecked utilization. On the other hand, opposition arises from the conscientious objectors who firmly believe that human life is fundamentally not interchangeable with economic metrics. These arguments prevailed in the recent debate over comparative effectiveness research (CER) in healthcare reform. “Cost” has been eradicated from CER either because of these political objections and/or because of the fundamental welfarist posture of the electorate. Meanwhile, many other nations have embraced the extra-welfarists approach, acknowledging that health eludes the standard notion of utility and transcends income and social norms.

Accordingly, committees established in other countries, such as the NICE, examine the cost effectiveness of treatments in determining the merit of offering them to the public. Whereas this approach works in the extra-welfarist climate, in the welfarist U.S. climate, this kind of governmental oversight involving equating human quality-of-life metrics with dollars is viewed with cynicism and suspicion and has been equated with “death panels.” Therefore, CER has been limited to comparative effectiveness limited to medical efficacy comparison without the semblance of human-to-economic value conversion that CEA invokes.

Value Based Purchasing and ICERs[edit]

Value Base Purchasing and Incremental Cost Effective Analysis - the sound of value-based approaches to the issues of reimbursement or pricing has been often heard across western countries. Among them are the patient access scheme in the Pharmaceutical Price Regulation Scheme (PPRS) reform in the UK, the ceiling price estimation using efficiency frontier in Institut fuer Qualitaet und Wirtschaftlichkeit im Gesundheitswesen (IQWiG) in Germany, and performance-based coverage in Centers for Medicate and Medicaid Services (CMS) in the United States.15 Over the next few years, it is expected that Asia will follow the western countries and seek a solution for value-based approaches to satisfy their own requirements. In the process of developing the policy, of course, pharmacoeconomics may have great potential to contribute to the debates in Asian nations. In Japan, once the price of a new drug is determined by the government in Japan, the new drug is also approved to be added to the National List for reimbursement. Since a constant reimbursement rate of 70% is applied automatically for all drugs after being listed on the National List, there is no room to discuss the issue of reimbursement rates after approval. Hence, value-based approaches to pricing and reimbursement for new drugs in Japan have been historically focused on how to improve the pricing equations made by the government. It has been argued that stratified cost-effectiveness analysis has a key role in reimbursement decision-making and value-based pricing (VBP). It has previously been shown that when manufacturers are price-takers, reimbursement decisions made in reference to stratified cost-effectiveness analysis lead to a more efficient allocation of resources than decisions based on whole-population cost-effectiveness analysis.16 The analysis of cost-effectiveness by subgroup provides the demand curve for the NHS and the price structure that can be used in VBP negotiations and maintained that this use of stratified cost-effectiveness analyses is essential if the NHS is to benefit from innovation in the short term. Reimbursement based on stratified, rather than whole-population, cost-effectiveness analysis has indeed been shown to lead to a more efficient use of healthcare when prices are fixed. Reimbursement or VBP processes that allow for negotiation regarding trade-offs between price and coverage may lead to improved outcomes, both for health-care systems and manufacturers, compared with processes where coverage is determined based on a stratified cost effectiveness at a given price.16

Comparing key measures utilized in comparative effectiveness research[edit]

The study of comparative effectiveness research (CER) is composed of measures that are useful in determining the value of various treatment options. While each of these measures provides a useful comparison of one treatment option versus another, they require different inputs into their respective calculations, thus the potential for producing conflicting results. In part, due to these challenges, there remains a widespread lack of understanding on the potential impact of CER in the U.S., and a reluctance to fully adopt the concept as part of our healthcare system.[12]

Historically, cost-benefit analysis (CBA) has been a measure widely used in the evaluation of various public projects, but less so in the evaluation of treatment options. CBA measures both costs and benefits in monetary terms, oftentimes requiring the researcher to place a dollar value on additional years of life and/or additional improvements in quality of life. The costs and benefits included in these analyses are not simply those that can be directly tied to the project, but also those that can indirectly be attributed to the project (i.e., externalities). The challenges and social discomfiture associated with placing a monetary value on human life have subsequently led to the creation of additional CER measures.[13]

Cost-effectiveness analysis (CEA) is similar to CBA in that it accounts for the costs of the treatments being researched. However, where CEA differs from CBA is that it does not attempt to derive the monetary value of the benefits associated with a particular treatment.[13] In essence, CEA, under the auspices of CER, effectively considers the benefits of each treatment option to be equal.

Cost-utility analysis (CUA), like CBA and CEA, quantifies the costs of a treatment as part of the equation. In considering the benefits of a treatment though, CUA goes further than CEA (which assumes benefits to be equal), but not as far as CBA (which applies a specific monetary value to benefits). CUA is unique in that it typically relies upon the quality-adjusted life-year (QALY) in an attempt to quantify the benefits of a particular treatment. The resulting quantifications of cost and QALYs are then used to derive the central measure of CUA, cost per QALY.[13]

References[edit]

Further reading[edit]

  • In 2009, The Institute of Medicine released Initial National Priorities for Comparative Effectiveness Research(CER). This report is designed to assist patients and healthcare providers across diverse settings in making more informed decisions. In this report, the Institute of Medicine's Committee on Comparative Effectiveness Research Prioritization establishes a working definition of CER, develops a priority list of research topics, and identifies the necessary requirements to support a robust and sustainable CER enterprise.
  • In April 2009, the New England Healthcare Institute released a white paper identifying ways to design and implement the new federal comparative effectiveness research program without stifling valuable innovation in health care.
  • Weinstein MC, O'Brien B, Hornberger J, et al. Principles of good practice for decision analytic modeling in health-care evaluation: report of the ISPOR Task Force on Good Research Practices—Modeling Studies. Value Health 2003; 6:9-17.
  • Hornberger J, Wrone E. When to base clinical policies on observational versus randomized trial data. Ann Intern Med 1997; 127(Part 2):697-703.
  • Weinstein MC, O'Brien B, Hornberger J, et al. Principles of good practice for decision analytic modeling in health-care evaluation: report of the ISPOR Task Force on Good Research Practices—Modeling Studies. Value Health 2003; 6:9-17.
  • Hornberger J, Robertus K. Comprehensive evaluations of healthcare interventions: The realism – transparency tradeoff. Med Decis Making 2005; 25:490-2.
  • Alexander GC, Lambert BL. Is Treatment Heterogeneity an Achilles’ Heel for Comparative Effectiveness Research? Pharmacotherapy. 2012;32:583-585.

External links[edit]