This JAMA Guide to Statistics and Methods reviews the use of cost-effectiveness analysis to quantify the tradeoffs in costs, harms, and benefits of new health care interventions compared with existing interventions.
Health care decision makers, including patients, clinicians, hospitals, private health systems, and public payers (eg, Medicare), are often challenged with choosing among several new or existing interventions or programs to commit their limited resources to. This choice is ideally based on a comparison of health benefits, harms, and costs associated with each alternative. How best to determine the optimal intervention is a challenging task because benefits, harms, and costs must be weighed for a given option and compared with alternatives.
One way to inform such decisions is to perform a cost-effectiveness analysis. A cost-effectiveness analysis is an analytic method for quantifying the relative benefits and costs among 2 or more alternative interventions in a consistent framework. In a study published in JAMA Oncology, Moss et al1 examined the cost-effectiveness of multimodal ovarian cancer screening with serum cancer antigen 125 compared with no screening in the United States, based on findings from the large United Kingdom Collaborative Trial of Ovarian Cancer Screening (UKCTOCS). The UKCTOCS evaluated the effect of screening on ovarian cancer mortality2 and demonstrated that multimodal screening reduced mortality among women without prevalent ovarian cancer.
THE USE OF COST-EFFECTIVENESS ANALYSIS
Choosing among alternative treatments or programs is complicated because benefits, harms, and costs vary in the following ways: (1) benefits may be reflected in varying patterns of reduced morbidity or mortality in patients; (2) interventions vary in price and also in costs of acquiring or providing them (eg, time costs); and (3) benefits and costs accrue differently to different constituents (patients, caregivers, clinicians, health systems, and society). A cost-effectiveness analysis is designed to allow decision makers to clearly understand the tradeoffs of costs, harms, and benefits between alternative treatments and to combine those considerations into a single metric, the incremental cost-effectiveness ratio (ICER), that can be used to inform decision making when limited resources are available.
DESCRIPTION OF COST-EFFECTIVENESS ANALYSIS
Cost-effectiveness analysis is an analytic tool in which the costs and harms and benefits of an intervention (intervention A) and at least 1 alternative (intervention B) are calculated and presented as a ratio of the incremental cost (cost of intervention A − cost of intervention B) and the incremental effect (effectiveness of intervention A − effectiveness of intervention B). This ratio is known as the ICER.
The incremental cost in the numerator represents the additional resources (eg, medical care costs, costs from productivity changes) incurred from the use of intervention A over intervention B. The incremental effect in the denominator of the ICER represents the additional health outcomes (eg, the number of cases of a disease prevented or the quality-adjusted life-years [QALYs] gained) through the use of intervention A over intervention B.3
QALYs are the most commonly used benefit measure in cost-effectiveness analyses, in which the length of life is left unchanged or adjusted downward to reflect the health-related quality of life. Specifically, a quality weight of 1 indicates optimal health, 0 indicates the equivalent of death, and weights between 0 and 1 indicate less-than-optimal health. The weight for each period is multiplied by the length of the period to yield the QALYs for that period.
A primary rationale for using QALYs as a standard effectiveness outcome in cost-effectiveness analyses is the ability for policy makers to compare ICERs for various interventions across different diseases when allocating scarce resources to the intervention(s) that provide the greatest value for money. ICER values that are low suggest that intervention A improves health at a small additional cost per unit of health, assuming that A is both more costly and effective than B. If the ICER is negative, interpretation is more complex because negative ICERs can result from negative incremental costs (ie, the new treatment is less costly than the existing treatment) or from negative incremental benefits (ie, the new treatment is less effective than the existing treatment). A new treatment is said to be “dominant” if it is lower in cost and more effective than the comparator and is clearly of better value for money. However, the new treatment is said to be “dominated” if it is higher in cost and less effective than the comparator and is not of good value for money.
LIMITATION IN THE USE OF COST-EFFECTIVENESS ANALYSIS
There are important qualifications to consider when reviewing a cost-effectiveness analysis. What is considered cost-effective depends on comparing the ICER to the threshold value (eg, $50 000 or $100 000 per additional QALY) of the decision maker, which represents the willingness to pay for a unit of increased effectiveness (eg, 1 QALY). The threshold helps to determine which interventions merit investment. This willingness to pay is often represented by the largest ICER among all the interventions that were adopted before current resources were exhausted, because adoption of any new intervention would require removal of an existing intervention to free up resources. There is no fixed threshold for cost per QALY to determine what is cost-effective. Most decision makers do not rely on a single threshold to determine investment decisions.
Cost-effectiveness analyses have numerous limitations, including that available data may be drawn from heterogeneous populations, data on important outcomes may be unavailable, and that only short-term outcomes may be available and long-term outcomes must be extrapolated. Further, simplifying assumptions often must be made about how to represent the health states associated with the disease being studied that may not accurately represent the nuance and complexities of the clinical setting.
In 2016, the Second Panel on Cost-Effectiveness in Health and Medicine4 recommended that all cost-effectiveness analyses should include a discussion of relevant limitations and efforts to compensate for the shortcomings of cost-effectiveness analyses. The Second Panel also recommended that all cost-effectiveness analyses should provide their findings from a health care sector perspective, which would incorporate the costs, benefits, and harms that are incurred by a payer, and from a societal perspective, which would incorporate all costs and health effects regardless of who incurs the costs or experiences the effects. To ensure that all consequences to patients, caregivers, social services, and others outside the health care sector are considered, the Second Panel recommended use of an “Impact Inventory” that lists the health- and non–health-related effects of an intervention. This tool allows analysts to evaluate categories of effects that may be most important to diverse stakeholders. Checklists for the various items that should be included when reporting cost-effectiveness analysis results were provided by the Second Panel.4
HOW WAS THE COST-EFFECTIVENESS ANALYSIS PERFORMED IN THIS STUDY?
Moss et al evaluated the cost-effectiveness of a multimodal screening (MMS) program for ovarian cancer in the United States from a health care sector perspective (eg, Medicare).1 In a health care sector perspective, only costs, health benefits, and harms that were observed by the health care sector are considered, and other costs, benefits, and harms that may affect patients or their caregivers are ignored.4
The authors developed a Markov simulation model using data from the UKCTOCS to compare MMS with no screening for women beginning at 50 years of age in the general population. The model, which involved a mathematical simulation that evaluated the benefits of the screening strategies in hypothetical cohorts of patients as they moved from one health state to the next, according to transition probabilities, demonstrated that MMS was both more expensive and more effective in reducing ovarian cancer mortality than no screening.
Clinical effectiveness was estimated from the UKCTOCS trial estimates of the effects of MMS on ovarian cancer mortality, with extrapolation of the long-term effects beyond the 11-year follow-up period. Direct medical costs were estimated based on Medicare claims data. Quality of life–related weights were included for the health states of being cancer free, undergoing MMS screening, and having ovarian cancer (incorporating lower weights for the chemotherapy and cancer stage).
HOW SHOULD THE COST-EFFECTIVENESS ANALYSIS BE INTERPRETED IN THIS STUDY?
In the main, base-case analysis, MMS screening with a risk algorithm cost estimate of $100 reduced ovarian cancer mortality by 15%, resulting in an incremental cost-effectiveness ratio of $106 187 per QALY gained (95% CI, $97 496-$127 793). The authors explored the uncertainty in the underlying parameters and found that screening women starting at 50 years of age with MMS was cost-effective in 70% of the simulations at a willingness to pay of $150 000 per QALY. If the willingness to pay were $100000 per QALY, then screening was cost-effective 47% of the time.
A cost-effectiveness analysis does not make the decision for patients, clinicians, health care systems, or policy makers, but rather provides information that they can use to facilitate decision making. A cost-effectiveness analysis is also not designed for cost containment. These analyses do not set the level of resources to be spent on health care, but rather they provide information that can be used to ensure that those resources, whatever the level available, are used as effectively as possible to improve health. When reviewing cost-effectiveness analyses, readers should examine the study and use the recommendations from the Second Panel on Cost-Effectiveness in Health and Medicine4 to help understand the implications of cost-effectiveness analysis research.
The following disclosures were reported at the time this original article was first published in JAMA.
Conflict of Interest Disclosures: Dr Maciejewski reported receiving research and center funding (CIN 13-410) from the VA Health Services Research and Development Service, receiving a contract for research from the National Committee for Quality Assurance, receiving research funding from the National Institute on Drug Abuse (RCS 10-391), and that his spouse owns stock in Amgen. Dr Basu reported consulting for Merck, Pfizer, GlaxoSmithKline, Janssen, and AstraZeneca as an expert on issues related to cost-effectiveness analysis. No other disclosures were reported.
LJ. Estimating cost-effectiveness of a multimodal ovarian cancer screening program in the United States: secondary analysis of the UK Collaborative Trial of Ovarian Cancer Screening (UKCTOCS). JAMA Oncol. 2018;4(2):190–195. Medline:29222541 doi:10.1001/jamaoncol.2017.4211
et al. Ovarian cancer screening and mortality in the UK Collaborative Trial of Ovarian Cancer Screening (UKCTOCS): a randomised controlled trial. Lancet. 2016;387(10022):945–956. Medline:26707054 doi:10.1016/S0140-6736(15)01224-6
et al. Recommendations for conduct, methodological practices, and reporting of cost-effectiveness analyses: Second Panel on Cost-effectiveness in Health and Medicine. JAMA[JAMA and JAMA Network Journals Full Text]
. 2016;316(10):1093–1103. Medline:27623463 doi:10.1001/jama.2016.12195