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Cost-Utility Analysis: 27,000,000 Possible Input Variants.

Gary C. Brown, MD, MBA, Melissa M. Brown, MD, MN, MBA, Peter Kertes, MD.

Evidence-Based Ophthalmology 2012;12:52-7.

Comparative effectiveness is defined by the US Congressional Budget Office [1] as ‘‘rigorous evaluation of the impact of different options that are available for treating a given medical condition for a particular set of patients. Such a study may compare similar treatments, such as competing drugs, or it may analyze very different approaches, such as surgery and drug therapy. The analysis may focus only on the relative medical benefits and risks of each option, or it may also weigh both the costs and the benefits of those options.’’

As the name implies, comparativeness effectiveness is simply the comparison of two or more interventions based upon any of multiple clinical parameters. [2] Although the Obama administration has emphasized the importance of comparative effectiveness, [1] there is currently no gold standard outcome, or criterion, by which to compare interventions. As healthcare interventions only deliver patient benefit by two means, improving quality-of-life and/or length-of-life, [3,4] a methodology which assesses both is most desirable.

Value-Based Medicine, as the name implies, is the practice of medicine based upon the standardized patient value (improvement in quality-of-life and/ or length-of-life) conferred by medical interventions. As such, it integrates quality-of-life in a quantifiable form based upon patient preferences for a normal health state versus current health.


Most primary outcomes of clinical trials to assess comparative effectiveness are specialty specific. For example, a cardiac clinical trial may evaluate the severity of coronary arterial disease or stroke reduced by an intervention, whereas a gastrointestinal clinical trial may assess the severity of gastroesophageal reflux disease reduced by an intervention. Needless to say, these two outcomes are difficult to compare.

Function-based quality-of-life instruments assess ambulatory function, social function, psychological function, and so forth. Commonly used function-based quality-of-life instruments include the Medical Outcomes Study Short-Form-36 questionnaire (SF-36) [6] and the National Eye Institute-Visual Functioning Questionnaire-25, [7] both of which provide  important information on comparative effectiveness within a given field of medicine. Nonetheless, they typically lack: 1) the ability to perform valid comparative effectiveness analyses across all specialties, 2) an all-encompassing overview of all factors related to quality-of-life, and 3) the ability to be integrated with costs in a commonly accepted fashion (cost-effectiveness analysis).

Preference-base quality-of-life instruments, [8] one and the same as utility analysis instruments, allow a patient to express which health state they prefer. Do they prefer to: 1) improve their health state by trading or risking something of value (time of life or money), or 2) remain in the same health state and make no trade or take no  risk.

Evidence-based to Value-Based Medicine. [5,8] It is the philosophy of Evidence-Based Ophthalmology that the best evidence-based medicine (EBM) should provide the foundation for knowledge which will allow practitioners to provide the highest quality of medicine for our patients. The highest quality EBM information typically comes from clinical trials. Trials with a low type 1 error (α < 0.05) and low type 2 error (β < 0.20) give the clinician the greatest confidence in the reliability (reproducibility) of the data presented. Thus, clinicians have the greatest conviction that the therapies they provide for their patients are the highest quality.

Although knowledge of a numerical or statistical change is critical, this information must be taken to another level to ascertain the true patient value of evidence-based discoveries. As an example, it was shown in the Branch Retinal Vein Occlusion Study [9] that laser treatment of macular edema occurring secondary to retinal vein occlusion yields a long-term mean visual acuity of 20/40 to 20/50, as compared with a mean visual acuity of 20/70 in the untreated control cohort. But equally as important is the question, ‘‘What does this mean for the quality-of-life for my patient?’’


Rather than dollars, the patient (human) value gained from an intervention refers to the improvement it confers in: (1) quality-of-life and/or (2) length-of-life. Therapies that do not accomplish at least one of these goals do not have a role in the battle against disease. The length-of-life component can often be extracted from evidence-based medical information in the literature. Quality-of-life information, however, is not so readily available. Why? Because the many instruments for measuring quality-of-life are not  applicable across diverse medical specialties and far from uniformly accepted.

The Quality-Adjusted Life-Year. In 1968, Klarman and associates [10] first used the Quality-Adjusted Life-Year (QALY). The QALY measures the total patient value conferred by an intervention. In 1977, Weinstein and Stasson [11] reported a methodology for ascertaining the cost-effectiveness of interventional medical therapies. It has since been modified to include the conversion of evidence-based medicine to Value-Based Medicine, as measured by patient-based preferences (utilities) and decision analysis. It then integrates costs to assess the financial value of the resources expended for the patient value gains. [5,12] An explanation of each of these variables follows below.


Utility analysis is a methodology to assess the quality-of-life associated with a health (disease) state. It is preference-based in that it allows patients to make a choice between two alternatives: (1) trading or risking something of worth (money, time of life, and so forth) to improve their health state, or (2) trading or risking nothing and remaining in the same health state.

By convention, an utility of 1.0 is associated with normal health permanently (or normal vision permanently for ocular diseases) and an utility of 0.0 is associated with death. The closer a utility to 1.0, the better the quality-of-life associated with a health state, whereas the closer to 0.0, the poorer the associated quality-of-life. Utilities have been obtained from physicians, administrators, researchers, and the general public, but increasing numbers of researchers [5,8,13-16] believe those obtained from patients are the most valid.

Utility variants. A number of instruments are available to measure utilities, including the standard gamble, willingness-to-pay, and time tradeoff methodologies. [5,17,18] Multi-attribute utility instruments, such as the EuroQol 5-D and the Health Utilities Index, ask questions in several domains, among which are mobility (walking), activities of daily living, self-care, pain and depression. Disutilities (loss of utility) associated with one or more domains are subtracted from 1.0 to arrive at an overall utility. [18] The authors herein have shown that the time tradeoff methodology is reliable and has construct validity. [5]

Time tradeoff utility analysis. With the validated and reliable time tradeoff technique, a patient is first asked how many years he or she believes they will live. The patient is then presented with the scenario that he or she could trade an amount of the remaining time of life in return for being rid of a disease entity. The proportion of time traded is subtracted from 1.0 to arrive at the utility. As an example, the average patient with counting fingers vision in the better eye is typically willing to trade approximately 50% of his or her remaining life in return for normal vision in both eyes. [19] Thus, such a patient with a 20-year life expectancy is generally willing to trade 10 years. The resultant utility is 0.50 (1.0 – 10/20). If a patient is willing to trade 2 of 10 remaining years, the utility is 0.80 (1.0 – 2/10). [19]

Ocular utilities. The quality-of-life associated with most ocular conditions can readily be ascertained with utilities. [19-25] They are most closely correlated with vision in the better-seeing eye. A vision utility of 1.00 is associated with 20/20 vision permanently, whereas an utility of 0.26 is associated with no perception of light bilaterally. [19,20]. Of note, bilateral 20/20 vision in each eye in people with ocular diseases is associated with autility of 0.97, rather than 1.00, as they have some degree of anxiety about their visual future. [21] Even second eyes are important. For example, people with ocular diseases with 20/20 to 20/25 vision bilaterally have an utility of 0.97, whereas those with 20/20 to 20/25 vision in one eye and poorer vision in the fellow eye have an utility of 0.89. [21]

Utilities are not static, and improvement of visual acuity by an interventional therapy yields an improvement in utility. For example, a patient with counting fingers vision in the better-seeing eye from a cataract who achieves 20/40 vision after cataract extraction typically improves from a time tradeoff utility of 0.52 to 0.80. Thus, there is a gain in utility of 0.28 as a result of the surgery. [19]

Utility respondents. The critical importance of utility respondents cannot be overemphasized. A study from the Center for Value-Based Medicine compared the opinions of age-related macular degeneration patients and physicians who treat these patients, other physicians and the general community. [16] Ophthalmologists who treat patients with age-related macular degeneration, underestimated the diminution in quality-of-life upon patients by 96% to 750%. [16]

What is then the gold standard? Utilities from patients! The old adage that one should walk a mile in another man’s shoes to understand his situation holds well with health-related quality-of-life and utilities.

QALY gains. [12,17,18] In addition to improvement gained from an interventional therapy, the duration of benefit also contributes to the patient value gain conferred by the therapy. The duration can be taken into account by using the QALY, which is derived by multiplying (the utility value gain obtained from an interventional therapy) x (the years of duration of the therapy). The cataract patient who improves from an utility value of 0.50 before surgery to 0.80 after surgery, and who experiences the benefit for the remaining 20 years of life, would thus gain a total of 6.0 (0.3 x 20) QALYs from the surgery.

This QALY gain can also be measured in percent improvement in patient value or percent improvement in quality-of-life for most ophthalmic interventions. Although this is not typically the case in ophthalmology, a therapy that improves length-of-life will also yield more QALYs, as the duration of benefit in years is used to derive the number of QALYs gained.

It should be noted that vehicles other than utility analysis are available for measuring quality-of-life. Most, such as the National Eye Institute-Visual Functioning Questionnaire-25 [5,7] and Medical Outcomes Survey Short Form-36 [5,6] are instruments that ask a number of set questions that are particularly task oriented. Although valuable in their own right, these vehicles are often not applicable across all specialties in medicine. Additionally, because of the specific number of set questions, they may miss variables related to the quality-of-life associated with a health state that are of unique importance to a patient. Utility analysis, in contrast, is believed to be more all encompassing because it incorporates all aspects associated with quality-of-life, including those that are task-specific, psychosocial, economic and so forth. [17]


Decision analysis allows a determination of the most probable utility outcomes by integrating the utility gain conferred by an intervention with the utility loss associated with adverse events. When various treatment options are available, decision analysis allows a determination of the optimal treatment strategy, based on the maximization of utility. Decision analysis can be combined with Markov modeling, [26] which takes into account recurrent risk, such as the yearly chance of developing choroidal neovascularization in a second eye in a patient with unilateral, neovascular macular degeneration.

Decision analysis is necessary because many variables contribute to an outcome. In the case of cataract surgery, the treatment can be complicated by macular edema, endophthalmitis, retinal detachment, and other adverse events. These all have an effect upon the final, mean visual acuity(and thus the utility) obtained after surgery. When the utility associated with a visual acuity is used in a decision analysis tree, the mean difference in utility gained from a therapy can be ascertained.


Amalgamating the costs associated with an interventional therapy with the number of QALYs gained from the therapy yields the cost per QALY gained ($/QALY) from that intervention. [27-29] This is the cost-utility ratio. Using the cataract example in which the patient gains 6.0 QALYs, and assuming the total cost is $6000 for the treatment, the resultant $/QALY is $1000.

Discounting. It should be noted that the costs associated with an interventional therapy must be discounted to account for the time value of money. This occurs because money has a changing value. [30] A million dollars invested today is worth more than a million dollars invested in five years for two reasons: 1) the million dollars invested today has the ability to generate a return over the next five years, and 2) inflation affects the overall value of the money. In healthcare, a year of life gained today and paid for today is a better bargain than a year of life gained in 10 years, but paid for today. [30] Thus outcomes, or QALYs gained, are also discounted to maintain the integrity of the simultaneous discounting of money. [5] Alternatively, the discounting of QALYs can be rationalized by considering the fact that good health now can be used to earn resources that will increase monetary gain over time. [5]

Discount rate. The rate of discounting is variable, but the Panel on Cost-Effectiveness in Health and Medicine [12] suggested that a 3% rate for healthcare economic analyses is probably appropriate. [5] The rationale for a 3% discount rate is that it represents the long-term amount dollars can earn with a safe investment (for example, a government bond at 5% minus the 2% level of annual inflation).

Cost-effectiveness analysis. This instrument integrates comparative effectiveness outcomes with their respective associated costs. Examples include years of life gained, years of good vision, or years of disability-free life integrated with their specific associated costs. The most sophisticated form of cost-effectiveness analysis is cost-utility analysis, which integrates QALYs with their associated costs. As is the case with comparative effectiveness, the lack of a criterion is a drawback to the current science of cost-effectiveness.

A source of confusion. One source of potential confusion in the healthcare economic arena should be clarified. Healthcare economists from the Panel for Cost-Effectiveness in Health and Medicine, [12] a group organized by the Public Health Service in the mid-1990s, refer to the $/QALY outcome analysis described herein as cost-effectiveness analysis. Other researchers, [5,17,18] including the current authors, refer to an analysis using the outcome of $/QALY, or the cost-utility ratio, as cost-utility analysis. Analyses using other outcomes, such as cost per life-year, cost per vision-year (year of good vision), or cost per disability-free year are referred to as cost-effectiveness analyses. The editors herein believe that cost-utility analysis is the preferred term for an analysis using the QALY as an outcome, as there is little uncertainty as to the type of analysis. Despite the term cost-utility analysis, the outcome is referred to as more or less cost-effective, rather than more or less cost-utilitarian.

A problem. Although there are many well-performed cost-utility analyses, there are very few in the current literature that are directly comparable. The reason for this discrepancy is the fact that there are many input variables that can be used in a cost-utility analysis. A summary of these variables is shown in the following section.


In this issue, we emphasize the different number of possible input variables that can go into a cost-utility analysis. Unfortunately, even one can considerably change the outcome. Each subcategory show below lists possible input variants.

Cost perspective: 1) societal, 2) third party insurer, 3) governmental, 4) patient. The societal perspective is that recommended by NICE (National Institute for Health and Care Excellence), the Panel for Cost-Effectiveness in Health and Medicine and the Center for Value-Based Medicine.

Number of possible variants = 4

Societal cost perspective input variables: 1) caregiver costs, 2) transportation costs, 3) residence costs owing to health, 4) inside activities of daily living, such as: paying bills, reading mail, shopping for food, preparing food, preparing and taking medications, and others, 5) outside activities of daily living, such as lawn mowing, trimming shrubs, painting the house, and others, 6) baby-sitting costs, 7) yearly wage loss or gain, 8) percent chance of employment and 9) volunteering costs. The caregiver costs should include paid and unpaid costs, as should transportation costs, babysitting costs, and activities of daily living costs. That said, an assessment of these costs generally differs from one analysis to another as primary data for these costs are often lacking.

Number of possible variants = 18+

Cost basis: 1) Medicare, average national rate, 2) Medicare, using different state and regional rates, 3) Medicaid rate, 4) commercial rate, 5) blend of Medicare and commercial rates, and 6) a blend of Medicare and Medicaid rates.

Number of possible variants = 6+

Utilities: 1) time tradeoff, 2) standard gamble, 3) willingness-to-pay, 4) Euro-QOL-5D, 5) Health-Utilities Index, 6) scaling methodologies, 7) Quality of Well-Being Scale, and 8) combinations of different utilities.

Number of possible variants = 8+

Utility respondents: 1) patients, 2) general public, 3) experts, 4) physicians, 5) caregiver surrogate respondents, 6) parent surrogate respondents, 7) administrators, 8) researchers and 9) others.

Number of possible variants = 9+               .

Discount rates: 1) 0%, 2) 3%, 3) 5%, 4) 10%, 5) another discount rate throughout an analysis, and 6) different discount rates for utilities and costs in an analysis.

Number of possible variants = 6+

Time of model: 1) mean time of survival, 2) one month, 3) three months, 4) six months, 5) one to 10 years, and 6) virtually unlimited others.

Number of possible variants = 18+

Currency: 1) United States, 2) Canada, 3) United Kingdom, 4) Australia, and 5) others.

Number of possible variants = 5+

Year of model: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10+ years before.

Number of possible variants = 11+

Total: Multiplying the variables in each subgroup reveals that at least 27,370,000 different input variables that can go into a cost-utility analysis. It quickly becomes apparent that a comparison of different cost-utility outcomes in the peer-reviewed literature is extraordinarily difficult, if not impossible. These many variables demonstrate the Center for Value-Based Medicine principal that: ‘‘THE MOST IMPORTANT ISSUE IN THE COMPARATIVE EFFECTIVENESS AND COST-EFFECTIVENESS ARENAS TODAY IS STANDARDIZATION.’’ Value-Based Medicine standardizes input variables to allow valid comparisons.


Value-Based Medicine, cost-utility analysis is a standardized methodology that creates a more relevant set of treatment standards than is possible with EBM alone. [5,8]

1) It measures the patient (human) value conferred by all interventions using the same outcome (QALY) and allows this value to be compared with that conferred by other interventions across all specialties, no matter how disparate.

2. The patient value conferred by an intervention includes all benefits gained from improvement in length-of-life and/or quality-of-life.

3. All benefits and all adverse events are included in calculating the patient value gain, alternatively known as the preference-based comparative effectiveness.

4. It utilizes the best evidence-based medicine, that which is most reliable (reproducible), from randomized clinical trials.

5. It is translational in that it takes evidence-based data to a higher quality of care by integrating patient quality-of-life opinions.

6. It allows a comparison of cost-utility across all medical specialties using the common denominator of $/QALY, the cost-utility ratio.

7. It highlights therapies tat provide great patient value, and also points out those that have negligible value, no value, or that may be harmful, so they can be improved or discarded.

8. It identifies interventions of equal human value, thus allowing healthcare stakeholders to give preference to those which are less costly.

9. It allows a comparison of interventional patient value gain across international borders.

10. It can quantify the financial value, or return-on-investment, for society (patients, insurers, government and so forth) for the direct medical costs expended using cost-benefit analysis.

It is the intention of Evidence-Based Ophthalmology to have a feature in each issue that highlights the value and/or cost-utility of an ocular intervention. It is our philosophy that knowledge and understanding of value are far better parameters than guesswork and anecdotes that guide us in certain of our therapies. We believe this is the path of the future,and that it is critical for physicians and other healthcare professionals to take the lead in establishing cost-utility (cost-effectiveness) analysis standards. Their knowledge and voice are critical for the best long-term interests of the most important people in the healthcare system—the patients.


1. Congress of the United States Congressional Budget Office. A CBO paper. Research on the comparative effectiveness of medical treatments. From the Internet @ http://www.cbo.gov/ftpdocs/88xx/doc8891/12-18-Comparative Effectiveness.pdf. (Accessed May 7, 2009).

2. Brown MM, Brown GC. Comparative effectivenessand translational medicine Evidence-Based Ophthalmol 2008;9:11-3.

3. Brown MM, Brown GC, Brown HC, et al. The comparative effectiveness and cost-effectiveness of vitreoretinal interventions. Curr Opin Ophthalmol 2008;19:202-7.

4. American Recovery and Reinvestment Act of 2009. NIH Challenge Grants in Health and Science Research (RFA-OD-09-003). From the Internet @ http://grants.nih.gov/grants/funding/challenge_award/. (Accessed April 22,2009).

5. Brown MM, Brown GC, Sharma S. Evidence-Based to Value-Based Medicine. Chicago, AMA Press; 2005.

6. Ware JE Jr, Sherbourne CD. The MOS 36-Item Short-Form Health Survey (SF-36). Medical Care 1992;30:473-83;

7. National Eye Institute. National Eye Institute Visual Functioning Questionnaire-25. From the Internet @http://www.nei.nih.gov/resources/vision function/vfq_ia.pdf. (Accessed June 26, 2009).

8. Brown MM, Brown GC, Sharma S, et al. Healthcare economic analyses and value-based medicine. Surv Ophthalmol 2003;48:204-223.

9. Branch Retinal Vein Occlusion Study Group. Argon laser for macular edema in branch vein occlusion. Am J Ophthalmol 1984;98:271–282.

10. Klarman H, Francis J, Rosenthal G. Cost-effectiveness applied to the treatment of chronic renal disease. Medical Care 1968;6:48-55.

11. Weinstein MC, Stasson WB. Foundations of cost-effectiveness analysis for health and medical practices. N Eng J Med 1977;296:716-21.

12. Gold MR, Siegel JE, Russell LB, et al. Cost-Effectiveness in Health and Medicine. New York, Oxford University Press; 1996.

13. Brown MM, Brown GC, Sharma S, et al. Evidence-based medicine, utilities and quality-of-life. Curr Opin Ophthalmol 1999;10:221-8..

14. Angell M. Patients’ preferences in randomized clinical trials. N Eng J Med 1984;310:1385-7.

15. Kassirer JP. Incorporating patients’ preferences into medical decisions. N Eng J Med 1994;330:1895-6.

16. Brown GC, Brown MM, Sharma S. Difference between ophthalmologist and patient perceptions of quality-of-life associated with age-related macular degeneration. Can J Ophthalmol 2000;35:27–32.

17. Redelmeier DA, Detsky AS. A clinician’s guide to utility measurement. In: Bergus GR, Cantor SB (eds.). Primary Care Clinics in Office Practice. Philadelphia, WB Saunders; 1995:22:271-80.

18. Drummond ME, O’Brien B, Stoddart GL, et al. Methods for the Economic Evaluation of Health Care Programmes, 2nd edition, New York, Oxford University Press; 1999.

19. Brown GC. Vision and quality-of-life. Tran Am Ophthalml Soc 1999;97:473-512.

20. Brown MM, Brown GC, Sharma S, et al. Utility values associated with blindness in an adult population. Br J Ophthalmol 2001;85:327-31.

21. Brown MM, Brown GC, Sharma S, et al. Quality-of-life associated with unilateral and bilateral good vision. Ophthalmology 2001;108:643–647.

22. Brown MM, Brown GC, Sharma S, et al. Quality-of-life associated with visual loss. Time tradeoff utility analysis comparison with systemic health states. Ophthalmology 2003;110:1076-81.

23. Brown MM, Brown GC, Sharma S, et al. Age-related macular degeneration. Its burden and a Value-Based Medicine analysis. Can J Ophthalmol 2005;40:177–187.

24. Brown MM, Brown GC, Sharma S, Landy J. Quality of lifewith visual acuity loss from diabetic retinopathy and age-related macular degeneration. Arch Ophthalmol 2002;120:481-4.

25. Hollands H, Lam M, Pater J, et al. Reliability of the time trade-off technique of utility assessment in patients with retinal disease. Can J Ophthalmol 2001;36:202-9.

26. Sonnenberg FA, Beck JR. Markov models in medical decision making. A practical guide. Med Decis Making 1993;13:322-8.

27. Brown GC, Brown MM, Sharma S, et al. Cost-effectiveness of treatment for threshold retinopathy of prematurity. Pediatrics 1999;104:e47..

28. Brown GC, Brown MM, Sharma S. Incremental cost-effectiveness of laser therapy for subfoveal choroidal neovascularization. Ophthalmology 2000;107:1374–1380.

29. Brown GC, Brown MM, Sharma S. Health care in the 21st century? Evidence-based medicine, patient preference-based quality and cost-effectiveness. QUal Management Health Care 2000;19:23-31.

30. Damodaran A. Corporate Finance. Theory and Practice. New York, John Wiley & Sons, Inc., 1997, pp 37-65.

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