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Education and debate
Taking account of future technology in cost effectiveness analysis Joshua A Salomon, Milton C Weinstein, Sue J Goldie Economic evaluations in health and medicine usually ignore the possibility of future advances in treatment. But when technological innovation is rapid such considerations can have major implications
Cost effectiveness analysis provides a tool for evaluating allocation of resources by characterising different healthcare interventions in terms of the extra cost per added unit of health benefit (box 1).1 Such analyses are being used increasingly to set national and international health priorities. The UK’s National Institute for Clinical Excellence, for example, is charged with guiding decisions on use of new and existing technologies in the NHS, based in part on cost effectiveness considerations.2 3 In recent years innovation in healthcare technology has occurred at an unprecedented pace for some problems, with new options rapidly supplanting existing interventions.4 We explore how cost effectiveness analysis could be extended to reflect evolving technologies, and how accounting explicitly for future treatment prospects might affect a typical analysis, using treatment for hepatitis C virus (HCV) infection as an example.
RUSSEL KIGHTLEY/SPL
Department of Population and International Health, Harvard School of Public Health, Harvard Center for Population and Development Studies, 9 Bow Street, Cambridge MA 02138, USA Joshua A Salomon assistant professor
Hepatitis C virus
Box 1: Cost effectiveness analysis The basic principle of a cost effectiveness analysis is that all consequences of decisions should be identified, measured, and valued. Cost effectiveness analysis provides a formal framework for comparing the relation between the health and economic consequences of different healthcare interventions. The results are summarised as an incremental cost effectiveness ratio. In this ratio, the net change in health outcomes associated with a particular strategy (compared with an alternative) is included in the denominator, typically expressed as quality adjusted life years (QALYs), and the net change in costs or resource use with a particular strategy (compared with an alternative) is included in the numerator. The incremental cost effectiveness ratio for a strategy is calculated in reference to the next most effective option, excluding strategies that are dominated (those with higher costs and lower benefits than other options) or weakly dominated (those with higher incremental cost-effectiveness ratios than more effective options).5 Interventions having incremental ratios of $50 000 (£27 500, €40 000) or $100 000 per QALY in the United States, or £30 000 (€44 000, $55 000) per QALY in the United Kingdom, are usually regarded as cost effective.6
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Treatment for hepatitis C virus infection An estimated 2.7 million Americans and 6.7 million Europeans have chronic HCV infection and are at risk of cirrhosis, end stage liver disease, and liver cancer.7–9 Treatment decisions are complicated by variable progression rates and require difficult trade-offs between costs, side effects, and uncertain clinical benefits.10–12 Various treatments have emerged in recent years, and a series of decision analyses have examined their cost effectiveness (table 1). Analyses typically have found that each new treatment has an attractive cost effectiveness ratio compared with many common interventions. Most ratios, in fact, have fallen below $10 000 (£5607, €8218)/QALY compared with the next most effective option. Given the rapid evolution of treatments for HCV infection, this example offers an illustration of how anticipated technological changes can be used to enrich conventional cost effectiveness analyses.
Department of Health Policy and Management, Harvard School of Public Health, Boston MA 02115, USA Milton C Weinstein Henry J Kaiser professor of health policy and management Sue J Goldie associate professor Correspondence to: J A Salomon jsalomon@ hsph.harvard.edu BMJ 2004;329:733–6
Technical details of the model and references w1-12 are on bmj.com
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Education and debate
Table 1 Evolving treatment regimens for chronic hepatitis C virus and findings on cost effectiveness Year approved
Treatment
Estimated efficacy (%)
Years of cost effectiveness analyses
Incremental cost effectiveness ratio ($/QALY)
1991
6-10
1995-1997
600-1900
w1,13
Interferon alfa, 48 weeks
1997
8-20
1997-2000
3200-9300
w2, w3
1998
30-45
1999-2002
500-7700
Peginterferon alfa plus ribavirin
2002
50-60
2002-2003
4200-35000
A Markov model comprises a set of mutually exclusive and collectively exhaustive health states. Each person in the model can reside in only one health state at any point in time, and all persons residing in a particular health state are indistinguishable from one another. Transitions occur from one state to another at defined recurring intervals (Markov cycles) of equal length (such as one month or one year) according to a set of transition probabilities. These probabilities may depend on population characteristics such as age, sex, and chronic disease and may vary over time. Values are assigned to each health state to reflect the cost and utility of spending one Markov cycle in that state. By synthesising data on costs, effects, and quality of life, a Markov model enables comparisons of the outcomes associated with different clinical strategies.15–16
Source
Interferon alfa, 24 weeks Interferon alfa plus ribavirin
Box 2: Markov model
w4-w9 12, w10-w12
Modelling the natural course of infection We developed a Markov model (box 2) of the natural course of HCV infection (figure), incorporating assumptions similar to those used previously.12–13 The model includes stages of fibrosis14 leading to clinical cirrhosis and its complications (such as decompensated liver disease and primary liver cancer), and the possibility of liver transplantation. Details on the model and data sources are on bmj.com.
interferon alfa and ribavirin would be available within three years and provide sustained viral clearance in 42% of treated patients versus 11% with monotherapy. To bias towards immediate monotherapy, we assume combination therapy will be more costly ($11 800 v $2500), have more severe side effects (yielding an average loss equivalent to 27 healthy days v 18), and that monotherapy will not reduce the effect of subsequent retreatment with combination therapy, which would also be provided after three years to people who had not responded to monotherapy. The results provide a sharp contrast to our naïve analysis using the conventional comparison with no treatment. With perfect knowledge of a future more effective regimen, a strategy of waiting three years for the new treatment would have lower costs and greater benefits than immediate treatment – that is, would “dominate” immediate treatment in the cost effectiveness idiom (table 2). With the key temporal component of this example, an important factor is the degree to which people “discount” the value of future consequences. Standard practice in cost effectiveness analysis applies annual discount rates of 3-5%, and the first set of results in table 2 reflects a 3% discount rate. Delaying treatment would defer costs and side effects but would also allow the disease to progress. If future consequences are discounted, the relative costs of immediate treatment are higher because of the timing; without discounting, costs are similar. For health outcomes, discounting reduces the advantages of immediate therapy because the benefits of treatment relate largely to averting future disease outcomes (discounting therefore compresses incremental gains from earlier treatment) and the negative effects of treatment (toxicities and adverse events) count less for deferred treatment when
Retrospective analysis We begin by travelling back in time to 1996, to revisit the decision problem confronting a patient with chronic HCV infection considering interferon monotherapy, the only approved treatment at that time. Adopting the conventional assumption that the “no treatment” strategy excludes the downstream potential for improved therapies, we calculated the incremental cost effectiveness ratio of monotherapy as $8700/ QALY gained compared with no treatment (see bmj.com for details of the model). With the benefit of hindsight, however, we might ask whether no treatment was the only relevant comparator. In other words, should the alternatives to immediate treatment also have included deferred treatment? A previous study considered one important element of this question, investigating watchful waiting with periodic liver biopsy versus immediate empirical treatment for chronic HCV infection.17 The authors found that immediate treatment was cost effective compared with biopsy management. We focus on an additional facet of this question—waiting and watching for technological innovation. For a patient in 1996, how would anticipation of imminent advances in treatment have changed the decision problem?
Perfect foresight scenario As a starting point, consider a perfect foresight scenario, in which a person must decide between immediate treatment, deferred treatment, or no treatment but we assume perfect knowledge about the timing and nature of a future improved treatment. Specifically, we assume combination therapy with
Table 2 Cost effectiveness of immediate treatment compared with deferred treatment in perfect foresight scenario Costs and benefits discounted at 3% Incremental cost effectiveness ratio ($/QALY)
No discounting Incremental cost effectiveness ratio ($/QALY)
Strategy
Cost ($)
No of QALYs
No treatment
13 100
17.81
Deferred treatment
19 600
18.49
9500
32 500
30.41
400
Immediate treatment
20 900
18.47
Dominated*
33 700
30.42
129 000
Cost ($)
No of QALYs
31 700
28.56
*A dominated strategy is one that is both more costly and less beneficial than another strategy.
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Education and debate discounted but count equally for immediate and deferred therapy without discounting. The net effect of these factors is that immediate therapy looks more attractive without discounting; it nevertheless retains a much less favourable cost effectiveness ratio compared with the conventional analysis (table 2). These results depend on several other key variables, including rates of progression of disease, costs and adverse outcomes associated with different regimens, and timing of new treatment options. Since this case is intended to illustrate a more general methodological question, we present just one example of the sensitivity of the perfect foresight results to important assumptions in the model. When we vary the year in which an improved treatment arrives, immediate treatment offers lower benefits than deferred treatment (at higher costs, making immediate treatment a “dominated” strategy) even if improvements are up to five years away. Moving from a one year to a five year delay, the difference in overall benefits between immediate and deferred treatment ranges from − 0.03 to − 0.002 QALYs—that is, losses equivalent to 110 healthy days if a new treatment would arrive in one year or one healthy day if the treatment were five years away.
Relaxing assumption of perfect foresight The above analysis is based on an unrealistic assumption of perfect knowledge of future treatments. However, uncertainty about emerging treatments can be incorporated in a decision analysis framework in the same way that other uncertain outcomes, such as developing cirrhosis, are captured. Define p as the probability of a new treatment being available in three years (with the specifications of combination therapy). In the deferred treatment strategy, we assume that individuals receive combination therapy in year 3 with probability p, or will fall back on monotherapy if no new treatment emerges that year, with probability (1 − p). In the immediate treatment strategy, individuals receive monotherapy now, but may (with probability p) have access to combination therapy in year 3, in the event of non-response or relapse. Allowing for this uncertainty, immediate treatment remains dominated by deferred treatment provided that p > 50%. The probability of a new treatment must be 30% or lower for the incremental cost effectiveness of immediate monotherapy to fall below $50 000/QALY (table 3).
Table 3 Incremental cost effectiveness of immediate therapy under different prospects for improved therapy Probability of improved treatment (%)
Incremental cost effectiveness ratio ($/QALY) Deferred treatment v no treatment
Immediate treatment v deferred treatment†
0
Dominated*
8 700†
20
9100
28 900
40
9300
92 400
60
9400
Dominated*
80
9500
Dominated*
100
9500
Dominated*
* A dominated strategy is one that is both more costly and less beneficial than another strategy. †When probability of improved therapy is 0, deferred treatment is dominated by immediate treatment, so incremental cost effectiveness ratio is shown for immediate treatment compared with no treatment.
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Summary points Cost effectiveness analyses normally do not take account of possible future advances in treatment Accounting for such possibilities can alter the conclusions of a cost effectiveness analysis greatly Uncertainties about new treatment can be reflected in a decision analysis in a way that is comparable with the modelling of other uncertain events
We have examined different scenarios regarding the timing and likelihood of better treatment separately, but we may also combine these two dimensions. For example, imagine that the cumulative probability of an improved treatment rises linearly over time so that there is a 10% chance of the new treatment emerging in one year, 20% in two years, and so on, up to 50% in five years. In this scenario, the incremental costs of immediate treatment compared with deferred treatment would be $800, with incremental benefits of 0.007 QALYs (less than 3 days), implying an incremental cost effectiveness ratio of more than $115 000/ QALY—that is, more than a 13-fold increase over the assumption of no potential improvements in treatment.
Conclusion Our analysis shows that taking account of possible future advances in treatment can change conclusions about the cost effectiveness of current interventions for conditions where technology is evolving rapidly. We have presented a simple model of HCV infection that does not do justice to its complex natural course or the nuances of treatment decisions because we wanted to illustrate a more general problem. Certain key features of the problem make consideration of evolving technologies important to economic evaluations, including progression over a relatively long period, uncertain efficacy and toxicity of current treatment, and steady scientific progress toward new treatments. Other conditions that share one or more of these features include chronic lymphocytic leukaemia, cystic fibrosis, primary pulmonary hypertension, and Parkinson’s disease. Our approach will be less relevant for conditions that require urgent treatment (for example, acute renal failure) or have current treatments that are very effective and well tolerated (for example, acute cardiac ischaemia). Technological change is one of many factors that can influence decisions regarding optimal timing of treatment. Incorporating the interactions between these various factors will enrich the clinical relevance of the analysis and enhance its utility in decision making. Quantifying uncertainties regarding technological innovation is methodologically challenging. Although prospective assessment of probabilities that specific improvements will occur is difficult, however, the implicit alternative is to assign them all probabilities of 0. At a minimum, plausible predictions of changes 735
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Education and debate in costs can be made in some cases based on the limited patent life of licensed pharmaceuticals. More sophisticated analyses are also possible incorporating additional dimensions of uncertainty such as response rates, costs, or stepped improvements over time. These complexities would be straightforward extensions of the conceptual logic presented here. Development of sound clinical guidelines, public health policy, and investments for new technology will require careful consideration of the incremental benefits, harms, and costs associated with new interventions—including those not yet discovered— compared with existing ones. Even at this early stage, we encourage analysts to model explicitly the full spectrum of alternative options, so that decision makers have estimates of their costs, benefits, and risks (including associated uncertainties) when faced with difficult choices about imperfect treatment. Minimally, economic evaluations should make explicit, and justify, the assumptions that are otherwise implicit, including no development of alternative technologies and no change in costs for existing technologies. Contributors and sources: JAS has worked extensively in the areas of priority setting, disease modelling, and health outcomes measurement, with a particular focus on global health. MCW is an expert on methods for cost-effectiveness analysis in health care and was co-chair of the US Panel on Cost-Effectiveness in Health and Medicine. SJG is an expert in disease modelling, cost effectiveness analysis, and technology evaluation, with an emphasis on viral infections and women’s health. This article was motivated by the authors’ previous work on evaluating the cost effectiveness of HCV treatment options. JAS conceived the study and is guarantor. JAS, MCW and SJG contributed to the analysis and writing. All authors approved the final version.
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Competing interests: None declared. 1 2 3 4 5 6
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Weinstein MC, Stason WB. Foundations of cost effectiveness analysis for health and medical practices. N Engl J Med 1977;296:716-21. Sculpher M, Drummond M, O’Brien B. Effectiveness, efficiency, and NICE. BMJ 2001;322:943-4. Birch S, Gafni A. The NICE approach to technology assessment: an economics perspective. Health Care Manage Sci 2004;7:35-41. Hammer SM. Increasing choices for HIV therapy. N Engl J Med 2002; 346:2022-3. Gold MR, Siegel JE, Russell LB, Weinstein MC, eds. Cost effectiveness in health and medicine. New York: Oxford University Press, 1996. Evans C, Tavakoli M, Crawford B. Use of quality adjusted life years and life years gained as benchmarks in economic evaluations: a critical appraisal. Health Care Manage Sci 2004;7:43-9. Alter MJ, Kruszon-Moran D, Nainan OV, McQuillan GM, Gao F, Moyer LA, et al. The prevalence of hepatitis C virus infection in the United States, 1988 through 1994. N Engl J Med 1999;341:556-62. Ray KW. Global epidemiology and burden of hepatitis C. Microbes Infect 2002;4:1219-25. Tong MJ, el Farra NS, Reikes AR, Co RL. Clinical outcomes after transfusion-associated hepatitis C. N Engl J Med 1995;332:1463-6. Seeff LB. Natural history of hepatitis C. Am J Med 1999;107:5-10S. Alter HJ, Seeff LB. Recovery, persistence, and sequelae in hepatitis C virus infection: a perspective on long-term outcome. Semin Liver Dis 2000;20:17-35. Salomon JA, Weinstein MC, Hammitt JK, Goldie SJ. Cost effectiveness of treatment for chronic hepatitis C infection in an evolving patient population. JAMA 2003;290:228-37. Bennett WG, Inoue Y, Beck JR, Wong JB, Pauker SG, Davis GL. Estimates of the cost effectiveness of a single course of interferon- alpha 2b in patients with histologically mild chronic hepatitis C. Ann Intern Med 1997;127:855-65. The French METAVIR Cooperative Study Group. Intraobserver and interobserver variations in liver biopsy interpretation in patients with chronic hepatitis C. Hepatology 1994;20:15-20. Briggs A, Sculpher M. An introduction to Markov modelling for economic evaluation. Pharmacoeconomics 1998;13:397-409. Drummond M, McGuire A, eds. Economic evaluation in health care: merging theory with practice. New York: Oxford University Press, 2001. Wong JB, Koff RS. Watchful waiting with periodic liver biopsy versus immediate empirical therapy for histologically mild chronic hepatitis C: a cost effectiveness analysis. Ann Intern Med 2000;133:665-75.
(Accepted 28 June 2004)
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Taking account of future technology in cost effectiveness analysis Joshua A Salomon, Milton C Weinstein, Sue J Goldie BMJ 2004;329:733-6 doi:10.1136/bmj.329.7468.733 http://bmj.com/cgi/content/full/329/7468/733
Supporting online materials: 1 2 3
Web figure Web references Technical appendix
http://bmj.com/cgi/content/full/329/7468/733/DC1
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Web figure
No fibrosis
Portal, no septa
Few septa
Hepatocellular carcinoma
Numerous septa
Cirrhosis
Ascites Liver transplant
Variceal bleeding Encephalopathy
Markov model of natural course of hepatitis C virus infection
2
Web references
w1
Dusheiko GM,.Roberts JA. Treatment of chronic type B and C hepatitis with interferon alfa: an economic appraisal. Hepatology 1995;22:1863-73. w2 Kim WR, Poterucha JJ, Hermans JE, Therneau TM, Dickson ER, Evans RW, et al. Cost effectiveness of 6 and 12 months of interferon-alpha therapy for chronic hepatitis C. Ann Intern Med 1997;127:866-74. w3 Shiell A, Brown S, Farrell GC. Hepatitis C: an economic evaluation of extended treatment with interferon. Med J Aust 1999;171:189-93. w4 Younossi ZM, Singer ME, McHutchison JG, Shermock KM. Cost effectiveness of interferon alpha2b combined with ribavirin for the treatment of chronic hepatitis C. Hepatology 1999;30:1318-24. w5 Buti M, Casado MA, Fosbrook L, Wong JB, Esteban R. Cost effectiveness of combination therapy for naive patients with chronic hepatitis C. J Hepatol 2000;33:651-8. w6 Wong JB, Poynard T, Ling MH, Albrecht JK, Pauker SG. Cost effectiveness of 24 or 48 weeks of interferon alpha-2b alone or with ribavirin as initial treatment of chronic hepatitis C. International Hepatitis Interventional Therapy Group. Am J Gastroenterol 2000;95:1524-30. w7 Sennfalt K, Reichard O, Hultkrantz R, Wong JB, Jonsson D. Cost effectiveness of interferon alfa-2b with and without ribavirin as therapy for chronic hepatitis C in Sweden. Scand J Gastroenterol 2001;36:870-6. w8 Sagmeister M, Wong JB, Mullhaupt B, Renner EL. A pragmatic and cost-effective strategy of a combination therapy of interferon alpha-2b and ribavirin for the treatment of chronic hepatitis C. Eur J Gastroenterol Hepatol 2001;13:483-8. w9 Stein K, Rosenberg W, Wong J. Cost effectiveness of combination therapy for hepatitis C: a decision analytic model. Gut 2002;50:253-8. w10 Wong JB,.Nevens F. Cost effectiveness of peginterferon alfa-2b plus ribavirin compared to interferon alfa-2b plus ribavirin as initial treatment of chronic hepatitis C in Belgium. Acta Gastroenterol Belg 2002;65:110-1. w11 Buti M, Medina M, Casado MA, Wong JB, Fosbrook L, Esteban R. A cost effectiveness analysis of peginterferon alfa2b plus ribavirin for the treatment of naive patients with chronic hepatitis C. Aliment Pharmacol Ther 2003;17:687-94. w12 Siebert U, Sroczynski G, Rossol S, Wasem J, Ravens-Sieberer U, Kurth BM, et al. Cost effectiveness of peginterferon alpha-2b plus ribavirin versus interferon alpha-2b plus ribavirin for initial treatment of chronic hepatitis C. Gut 2003;52:425-32.
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Technical appendix
The model structure and parameter values used in this analysis were adapted from previous studies (tables A1 and A2).1-20 Disease progression was simulated in a cohort of 40-year-old patients with elevated liver enzyme levels, positive results on quantitative HCV RNA assays and serological tests for antibody to HCV, and no evidence of fibrosis on liver biopsy, under different possible treatment scenarios. Health states included early histologic stages of liver disease classified using the METAVIR scoring system,21 which characterizes the extent of fibrosis that results as damaged liver cells are repaired, including no fibrosis, portal fibrosis without septa, portal fibrosis with few septa, and numerous septa without cirrhosis. Long-term complications were defined clinically as compensated cirrhosis, decompensated cirrhosis (ascites, variceal hemorrhage and hepatic encephalopathy) and primary hepatocellular carcinoma (HCC) (figure). Transition probabilities determined the movements of patients through different health states until all members of the cohort had died. Each year, patients faced probabilities of fibrosis progression, complications from cirrhosis, and competing mortality risks from decompensated cirrhosis, HCC and other causes unrelated to HCV. Patients with decompensated cirrhosis could receive an orthotopic liver transplantation. In a previous study,4 we used progression parameters that were age- and sex-specific and were empirically calibrated to observed epidemiologic data.22 For simplicity in the present study, we have taken an average rate of fibrosis progression based on the literature.6-8 Other rates and probabilities deterTable A1. Model parameter values: rates and probabilities Variable Annual rate (per person) * Remission†5 Fibrosis progression‡6-8 Cirrhosis to ascites1 Cirrhosis to variceal hemorrhage1;9 Cirrhosis to hepatic encephalopathy1;9 Cirrhosis to hepatocellular carcinoma1;9 Annual mortality rate (per person)* Ascites1;10 Variceal hemorrhage (first year / subsequent)1;11 Hepatic encephalopathy (first year / subsequent)1;12 Hepatocellular carcinoma13 Treatment response probabilities Genotype 1 Interferon monotherapy14;15;17;18 Interferon & ribavirin14-16 Other Genotypes Interferon monotherapy14;15;17;18 Interferon & ribavirin14;15 Treatment mortality probability19 Liver transplant probability1 * Annual
Value 0.006 0.133 0.025 0.011 0.004 0.015 0.11 0.4 / 0.13 0.68 / 0.40 0.6
0.06 0.31 0.26 0.67 0.0005 0.031
rates are converted into annual probabilities in the model. occurs from the “no fibrosis” state only. progression rate applies to progression from no fibrosis to portal fibrosis without septa, from no septa to few septa, and so on, including progression from numerous septa without cirrhosis to cirrhosis. † Remission ‡ Fibrosis
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Table A2. Model parameter values: costs and quality of life Variable Treatment costs (2001 US $)*,† Genotype 1 Interferon monotherapy20;3 Interferon & ribavirin20;3 Other Genotypes Interferon monotherapy20;3 Interferon & ribavirin20;3 Costs of annual care (2001 US $)† Chronic hepatitis C1 Compensated cirrhosis1 Ascites1 Variceal hemorrhage, first year1 Variceal hemorrhage, subsequent1 Hepatic encephalopathy, first year1 Hepatic encephalopathy, subsequent1 Hepatocellular carcinoma1 Liver transplant, first year1 Liver transplant, subsequent1 Health-related quality of life weights Mild chronic hepatitis C‡2 Moderate chronic hepatitis C‡2 Compensated cirrhosis2 Ascites2 Variceal hemorrhage2 Hepatic encephalopathy2 Hepatocellular carcinoma2 Liver transplant2
Value
2,145 12,743 3,442 9,574 123 895 3,765 20,822 4,075 13,365 3,096 35,917 118,285 20,657 0.98 0.92 0.82 0.65 0.55 0.53 0.55 0.86
* Costs
for interferon monotherapy and combination therapy do not include pre-treatment costs because the target population includes patients whose serological and histological statuses have already been identified. † All costs have been adjusted to 2001 U.S. dollars using the medical care component of the Consumer Price Index. ‡ The health-related quality of life weight for mild chronic hepatitis is applied to no fibrosis and portal fibrosis without septa, and the weight for moderate chronic hepatitis is applied to portal fibrosis with few septa and portal fibrosis with numerous septa but without cirrhosis.
mining progression in the model were derived from published studies (table A1). Estimates for treatment efficacy were computed from pooled results of randomized, controlled trials (table A1).14-18 Based on accumulated evidence of a strong link between virological and histological endpoints,23-29 the principal endpoint of interest in most studies has been clearance of HCV RNA, referred to as a virological response, measured both at the completion of treatment (end-of-treatment response) and six months after treatment completion (sustained response). We assumed the following: (1) chronic HCV infection may resolve spontaneously or through successful treatment, in either case implying clearance of HCV RNA; (2) spontaneous resolution occurs only in individuals without fibrosis; (3) patients with sustained response to treatment do not experience subsequent histologic progression of fibrosis. Annual costs for patients in each of the clinical states in the model (table A2) were derived from a published study1 that included detailed estimates of resource utilization, including hospitalizations, out-
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patient visits, laboratory tests and medications and interventions. Treatment costs were based on average wholesale drug costs,20 combined with previously published cost estimates for clinic visits, laboratory tests and the treatment of adverse events.3 The costs of therapy accounted for the discontinuation of treatment in patients who did not experience a virological response after 12 weeks of monotherapy or 24 weeks of combination therapy, and also in patients who suffered moderate to severe adverse events.3 The costs of time spent receiving medical care have not been included in the model, although they were assumed to be small relative to the costs of medications and treatment interventions. Health-related quality of life weights for the different health states in the model (table A2) were drawn from a previous study that elicited values from a panel of hepatologists.2 Weights for HCV-related states were assumed to be independent of other comorbidities. The quality of life weight for a given age, sex and HCV state was computed as the product of the weight associated with an HCV-specific health state and an average age- and sex-specific quality weight obtained from published data.30 Previous estimates of the reduction in quality of life (or disutility) associated with treatment side effects have spanned a wide range, from 0.02 to 0.50.2;31 We used a conservative estimate of 0.05 for the disutility of interferon monotherapy and assumed the disutility of combination therapy was 50% greater (0.075) in order to bias the analysis in favor of immediate rather than deferred therapy. 1 2 3
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