Original Contribution

The American Journal of Gastroenterology (2005) 100, 643–651; doi:10.1111/j.1572-0241.2005.40976.x

Estimation of Utilities for Chronic Hepatitis C from SF-36 Scores

Hla-Hla Thein MD, MPH1, Murray Krahn MD, M Sc1, John M Kaldor PhD1 and Gregory J Dore MD, PhD1

1National Centre in HIV Epidemiology and Clinical Research, The University of New South Wales, Sydney, NSW, Australia; and Departments of Medicine and Health Policy, Management and Evaluation, University of Toronto, Ontario, Canada

Correspondence: Gregory J Dore, MD, PhD, National Centre in HIV Epidemiology and Clinical Research, The University of New South Wales, Level 2, 376 Victoria Street, Darlinghurst NSW 2010, Australia

Received 6 May 2004; Revised  0000; Accepted 8 September 2004.

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Abstract

OBJECTIVES:

 

Utilities are the recommended health-related quality of life (HRQOL) measure for cost-effectiveness studies. The aim of this study was to estimate utilities for chronic hepatitis C health states from published studies reporting Short Form-36 (SF-36) quality-of-life scores.

METHODS:

 

A systematic review of published studies was undertaken. Articles were eligible for review if direct HRQOL assessment using the SF-36 in a chronic hepatitis C population was reported. SF-36 data were grouped according to hepatitis C virus (HCV) treatment status at baseline, stage of liver disease, association with comorbidity, and HCV treatment response. The SF-36 scores were then transformed into utilities using three different methods.

RESULTS:

 

Using Nichol's method, the estimated SF-36 mean utilities were 0.87 for sustained virological response (SVR) to interferon-based treatment, 0.82 for untreated chronic hepatitis C, 0.81 for precirrhosis, 0.76 for compensated cirrhosis, 0.69 for decompensated cirrhosis, 0.67 for hepatocellular carcinoma (HCC), and 0.77 for liver transplant. Other methods showed differences across comparison groups (e.g., treated vs untreated) but absolute scores differed substantially by translation method. SF-36 utilities for different stages of liver disease varied considerably from expert estimates but comparable to direct patient-elicited utilities.

CONCLUSIONS:

 

Application of SF-36 translation methods facilitate use of large existing datasets to generate community-weighted utilities for cost-effectiveness analyses, an important consideration in the absence of large studies of direct patient-elicited utilities in chronic hepatitis C.

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INTRODUCTION

A minority of people with chronic hepatitis C virus (HCV) infection will develop life-threatening long-term sequelae such as liver failure and hepatocellular carcinoma (HCC) (1). However, in earlier stages, HCV seems to impair health-related quality of life (HRQOL) in the absence of severe liver pathology (2,3,4,5,6). HRQOL in chronic hepatitis C has largely been assessed using the Short Form-36 (SF-36) Health Survey (7), a health status instrument that measures morbidity of a disease state by providing a self-reported profile of health outcomes in eight defined domains (see Appendix A). Generic health measures such as SF-36 encompass broad domains of HRQOL but do not directly measure individual preferences for health outcomes.

An alternative approach to HRQOL involves the measurement of utilities, to indicate preferences for particular health outcomes (8,9). Utilities measure health on a scale extending from 0 (usually immediate death) to 1 (usually full health), and allows both morbidity and mortality to be combined into a single weighted measure, the Quality-Adjusted Life Year (QALY) (8,9,10). Thus, utility measures are widely employed in cost-effectiveness analyses (8,9,10,11,12,13) and are also used as outcome measures in randomized trials and population health surveys (14,15). Utilities can be elicited directly from individuals or surrogates affected by the health states of interest using standard scaling methods such as the time trade-off (TTO) or standard gamble (SG) (8,9,11,16). Utilities can also be obtained indirectly using questionnaires like the Health Utilities Index (HUI) that incorporate community derived utility weights (further description in methods section) (11,17,18,19).

For cost-effectiveness studies in chronic hepatitis C, most published utilities have been obtained from surrogates, usually a panel of hepatologists (20). Surrogate estimates of utilities from expert judgements (20,21,22,23,24,25,26,27,28,29,30) may differ widely from patient-elicited utilities for the same health state. A Canadian study (31) found that patient-elicited utilities were lower than previous expert estimates for mild-to-moderate chronic hepatitis C and sustained virological responders, but higher for advanced liver disease complications such as decompensated cirrhosis and HCC. Such discrepancies may have a substantial impact in cost-effectiveness analyses (32).

In the absence of patient-derived utility data for chronic hepatitis C, various methods (33,34,35,36,37,38) have been proposed to generate utilities from scales such as the SF-36, which were not originally designed as utility instruments. Although this represents a somewhat indirect method of obtaining utility scores, it opens up the vast quality-of-life literature generated using nonpreference-based instruments to researchers and clinicians who need utility scores for their research, or who simply wish to understand HCV outcomes using a preference-based perspective. The aim of this study was to estimate utilities for chronic hepatitis C at different disease stages both prior to and after HCV treatment, using published patient-derived SF-36 scores.

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METHODS

Literature Search

We sought published papers that measured HRQOL using the SF-36 in individuals with chronic hepatitis C. The MEDLINE, EMBASE, and PUBMED databases were searched up to August 2003 for English-language articles using "hepatitis C,""health-related quality of life," and "quality of life" as keywords. Papers cited in the references of primary articles were also reviewed. To qualify for inclusion, papers must have reported sufficiently detailed SF-36 data to permit the estimation of mean utility scores, using the methods described below. Data abstracted for each paper included primary author, year, study site, sample size, mean age, hepatitis C disease stage, and HCV treatment. A total of 19 studies of distinct study populations met the inclusion criteria and were included in the review.

Translation of SF-36 Data to Utilities

There are three published methods that can be used to derive utilities from grouped SF-36 data available in published reports (33,34,35). Other methods (36,37,38) require individual patient-level SF-36 data. In order to estimate utilities from the SF-36 data in the papers obtained through the literature search, we used the three methods that could be applied to grouped data. The methods described by Nichol (33) and Fryback (34) require inputs of mean scores from the various domains of the SF-36. These scores are then transformed into a utility using an empirically derived regression formula. The method of Shmueli (35) is much simpler, in that it derives the utility by transforming the overall SF-36 score.

From each study that reported on SF-36 in people with chronic hepatitis C, mean scores for each domain of the SF-36 were abstracted where available for subpopulations defined by hepatitis C disease stage and treatment status. The mean SF-36 scores for these subpopulations were then translated into utilities, using the three methods, via the equations presented in Appendix A.

The methods of Fryback and Shmueli use SF-36 scores on a scale of 0–100, whereas Nichol's method requires norm-based scores (NBS) that have been standardized to a mean of 50 and a standard deviation of 10 in the general U.S. population (39). Studies that reported SF-36 on a scale of 0–100 were transformed to NBS using 1998 U.S. population norms, and those that provided SF-36 NBS were transformed into 0–100 scores using algorithms provided in the SF-36 summary measures manual (39). For the utilities calculated using the Nichol and Fryback methods, 95% confidence intervals were estimated by subtracting and adding the incremental values for 95% confidence intervals (i.e., based on sample sizes) to the predicted utilities (33,34).

Comparison of Different Types of Utilities

We then compared the estimated SF-36 utilities with utilities derived from experts (Appendix B) and patients (31) reported in other published studies. SF-36 utilities were derived from the three methods (33,34,35) that applied cross-translation methodology between SF-36 scores and the HUI2 (11,17), the Quality of Well-Being Scale (QWB) (18), and visual analogue scales (VAS) (8,16), respectively. Patient-elicited utilities (31) were derived directly from patients with chronic hepatitis C using HUI3 (17), VAS (8,16), SG (16), and EuroQol Index (EuroQolIndex) (40).

SG, TTO, and VAS are the three direct patient-elicited methods used most frequently to measure utilities (8,16). The SG method involves an individual's choice between two alternatives, a certain outcome and a gamble, where the certain outcome is intermediate in desirability between the best and worst gamble outcomes. The utility of that health state is one minus the probability of death at the point of indifference between the two alternatives (8,9,11,16,19). Similar to the SG, the TTO technique is a choice between two alternative health states: one alternative is live in the health state under consideration for the remainder of life, while the other alternative is to have a shorter but fully healthy life. The number of years of healthy life are varied until the individual is indifferent to accepting a shorter healthy life or a longer life in a less desirable health state. A utility score is then obtained by dividing the number of years of healthy life an individual was willing to trade by the number of years the individual was expected to live in the current health state (8). In the VAS, a health state is valued by placing a mark on a 100-mm horizontal or vertical line with the best imaginable health (usually full health) at one end and the worst imaginable health (usually death) at the other. The utility for that health state is achieved by dividing the distance from death to the health state by the distance from death to full health (8,16,19).

Indirect measurement of utilities employs questionnaires incorporating weights. Questionnaires consist of several health domains (e.g., pain) and levels (e.g., no pain, a little pain). Each level in each domain has a weight, derived from previous studies, that allows the entire questionnaire to be scored to generate a zero to one utility score for an individual's current health status (11,17). The HUI measures several attributes of health state classification and uses SG-based community preference weights (11,17). The QWB weights scale scores for mobility, physical activity, social activity, and symptoms or problems using community-derived preference weights obtained by rating scale measurements (18,19). Similarly, the EuroQolIndex measures five health domains including mobility, self-care, usual activities, pain/discomfort, and anxiety/depression and EuroQol utilities are derived using community-derived preference weights obtained by time trade-off method (19,40). Indirectly elicited utility scores are widely used in cost-effectiveness analyses and are considered the preferred measures for economic evaluation (13).

To facilitate comparisons between different types of utilities and ease of interpretation, mean utilities were calculated. For SF-36 utilities, means weighted by subpopulation sample size were calculated where utilities for a particular disease state were available from more than one study. Mean utilities for different hepatitis C disease states were calculated for expert estimates (Appendix B) by averaging the mean utilities across the number of studies.

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RESULTS

Mean utilities for chronic hepatitis C translated from grouped SF-36 scores using the methods of Nichol, Fryback, and Shmueli are summarized in Table 1 and 2. SF-36-derived utilities are reported according to: (i) stage of liver disease, (ii) comorbidity, and (iii) HCV treatment response. Fryback's method produced lower utilities than Nichol's method for all stages of chronic hepatitis C, while Shmueli's method generally gave higher utilities than both the other methods.



Stage of Liver Disease

SF-36 utilities for different stages of chronic hepatitis C are reported in Table 2. The weighted mean utilities estimated from Nichol, Fryback, and Shmueli's methods were 0.81, 0.70, and 0.86, respectively, for precirrhosis; 0.76, 0.67, and 0.84, respectively, for compensated cirrhosis; and 0.69, 0.63, and 0.80, respectively, for decompensated cirrhosis. The mean utilities were 0.67, 0.60, and 0.77, respectively, for HCC and 0.77, 0.66, and 0.83, respectively, for liver transplant. Utilities for undiagnosed hepatitis C were higher than those obtained in people who had received a diagnosis (mean scores: Nichol: 0.86 vs 0.77; Fryback: 0.74 vs 0.69; Shmueli: 0.89 vs 0.83). Asymptomatic (HCV detected in individuals during routine screening for blood donation or during medical insurance examinations) and symptomatic (patients presenting with symptoms and were subsequently found to be infected) chronic hepatitis C patients (2) had similar utilities (mean scores: Nichol: 0.76 vs 0.77; Fryback: 0.68 vs 0.69; Shmueli: both 0.83). SF-36 utilities for people with subclinical neurocognitive dysfunction were, however, lower than those without (mean scores: Nichol: 0.72 vs 0.80; Fryback: 0.65 vs 0.70; Shmueli: 0.82 vs 0.86).

Comorbidity

The mean utility score for people with chronic hepatitis C who had never injected illicit drugs was higher than that of individuals who had injected previously (mean scores: Nichol: 0.80 vs 0.75; Fryback: 0.70 vs 0.68; Shmueli: 0.85 vs 0.82, Table 2). Similarly, utilities for chronic hepatitis C without comorbidity were higher than those with psychiatric or medical (including human immunodeficiency virus (HIV)) comorbid illnesses.

Outcome of HCV Treatment

SF-36 utilities for chronic hepatitis C prior to, during, and following interferon-based treatment are shown in Table 3. Mean utilities were reduced by 0.01–0.06 units during treatment, with standard interferon therapy giving slightly lower utilities than pegylated interferon. Following 24 wk posttreatment, SF-36 utilities exceeded pretreatment levels, gaining 0.03–0.04 units among those who achieved an SVR.


Relapsers or nonresponders to previous interferon treatment had similar SF-36-translated utilities to people with untreated chronic hepatitis C (Nichol: 0.83 vs 0.82; Fryback 0.72 vs 0.70; Shmueli: 0.88 vs 0.86, Table 1).

Comparison of SF-36 Utilities with Expert Estimates and Direct Patient-Elicited Utilities

SF-36-derived mean utilities were lower than mean expert estimates for SVR (0.74–0.90 vs 0.96) and precirrhosis (0.70–0.86 vs 0.87), but considerably higher for decompensated cirrhosis (0.63–0.80 vs 0.49) and HCC (0.60–0.77 vs 0.26, Table 4). Furthermore, SF-36 utilities produced a narrow range across the health states (0.04–0.20 units) compared to a wide range from expert estimates (0.09–0.70 units).


SF-36 mean utilities, particularly those derived using Nichol's method, were broadly comparable to direct patient-elicited utilities (Table 4). When SF-36 utilities were compared with patient-elicited utilities using similar scaling technique, Nichol's HUI2-derived utilities were close to patient-elicited utilities using HUI3 (31), except for one health state, HCC (0.67 vs 0.51). Similarly, Fryback's QWB-derived utilities were comparable to patient-elicited VAS utilities. In contrast, Shmueli's VAS-derived utilities were much higher than patient-elicited VAS utilities.

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DISCUSSION

We estimated the effect of disease stage, medical and psychiatric comorbidity, and HCV treatment on utility values for chronic hepatitis C health outcomes using published SF-36 datasets and three separate translation algorithms. Knowledge of diagnosis of HCV infection, advanced liver disease, psychiatric and/or medical comorbidity, injection drug use, and subclinical neurocognitive dysfunction was associated with lower utilities compared to the absence of these health states. HCV treatment was associated with short-term reduction in utilities of 0.01–0.06 units from pretreatment estimates. A gain of 0.03–0.04 units was achieved with an SVR. This gain of 0.03 in mean utility with SVR following HCV treatment is small but is similar to the utility gain in middle-aged persons without arthritis (0.05), back pain (0.05), cataract (0.04), or hypertension (0.01) (56).

Our study demonstrates that researchers can use the substantial body of SF-36-based outcomes research to generate meaningful health utilities. These data were obtained from a wide variety of clinical settings and patient populations, and may therefore be more broadly generalizable than small datasets of patient-derived utilities obtained at a single institution (31). Additionally, the present study confirmed previous findings (31) that patient-derived utilities are lower than previous expert estimates for SVR (0.87 vs 0.96) and precirrhosis (0.81 vs 0.87), but much higher for decompensated cirrhosis (0.69 vs 0.49) and HCC (0.67 vs 0.26, Table 4). Evaluation of cost-effectiveness of HCV treatment for early chronic hepatitis C suggests that cost-effectiveness ratios are highly sensitive to the assumptions of utility scores associated with HCV-related liver disease and treatment (57). In their evaluation, Salomon et al. used expert panel estimates and may have underestimated the cost-effectiveness of HCV treatment. Accurate measurement of utility appears to be imperative in chronic conditions such as chronic hepatitis C (58), particularly as treatment-induced sustained viral clearance is associated with regression of liver disease and improvement in HRQOL (59). Use of alternative utility scores generated from this study would most likely have a considerable impact on the cost-effectiveness ratios estimated by studies such as Salomon et al.

There are several limitations in translation of utilities from SF-36 scores. Our literature search did not include unpublished studies or seek additional information from authors when not available in published manuscript. This may have introduced potential publication bias in favor of positive outcomes particularly regarding the effect of treatment. Health status scales such as SF-36 and utilities measure different components of health. Health status scales measure dimensions of HRQOL and provide descriptive information of health outcomes of interest whereas utility measures include an evaluative component and are designed primarily for application in cost-effectiveness analyses. Although both measures are utilized to evaluate health outcomes, they are not easily interchangeable measures of HRQOL. A systemic review by Revicki and Kaplan suggests that health status scales modestly correlate with preference measures (60). Translation methods to estimate utilities from SF-36 scales thus represent an indirect and imperfect method of obtaining utilities.

Different methods yielded different utilities for chronic hepatitis C. The method proposed by Fryback et al. only utilized five of the eight scales of the SF-36 (physical functioning, role physical, bodily pain, general health, mental health). This may explain the lower utilities derived with this method compared to Shmueli's and Nichol et al.'s methods. However, Fryback et al. and Shmueli's methods have been validated in other conditions (61). All three methods showed a moderate degree of correlation (regression models explain around 50% of the variance) between the SF-36 and utility based instruments (33,34,35). Such suboptimal correlation suggests that the attributes of nonpreference-based instruments such as the SF-36 are not fully congruent with individuals' preferences for health outcomes, the attributes employed in preference-based instruments, or both. Notably, a study among patients with low back pain using both individual level and mean level models (34,35,38,61) showed high agreement in utilities between the models that used preference-based methods (i.e., SG and TTO) but less agreement between the nonpreference-based methods (i.e., QWB, VAS). This suggests that variability in utility estimates using nonpreference methods depends on the type of SF-36-derived algorithm but not necessarily in favor of the individual level data over the mean level data.

There is no standardized method for eliciting utilities. The three methods used in this study are based on alternatives of the "gold standard," namely HUI, QWB, and VAS. A recent study (62) comparing SG utility scores to HUI2 scores showed agreement between the two at the group mean level suggesting that HUI2 scores may be a good surrogate for SG scores. The QWB and VAS appear to yield valid scale values comparable to any other methods on large sample studies (16,19).

Utilities for some disease states such as liver failure and HCC have not been adequately estimated from patient surveys as these conditions were exclusion criteria for participation in treatment-based studies. Direct patient-elicited utilities for advanced liver disease complications and liver transplant have recently been produced (31), but larger studies with utilities directly elicited from patients with such disease states are needed for verification.

The question remains which utilities should be used for cost-effectiveness analyses and under which circumstances. The Panel on Cost-Effectiveness in Health and Medicine (13) has generally advocated the use of community-weighted utilities for cost-effectiveness analysis. This study offers a significant contribution as it provides a number of estimates of community-weighted utilities derived in a large number of patient populations and clinical contexts. Utilities were derived using instruments that incorporate community preference weights. In this sense, utility of an individual or mean utility of a group are "community derived" if an instrument like the HUI is used, regardless of the composition of the individual or group that is completing the instrument. Although this approach is indirect, it has the virtue of broad generalizability and opens up a vast amount of the existing HCV outcomes literature to alternative uses. Among the available methods of generating community-weighted utilities from patient-elicited HRQOL information, we suggest that Nichol's utilities (33) may be preferred, as they are based on the SG, the reference standard and because the utilities derived from this method appear to approximate the direct patient-elicited utilities (31) somewhat more closely than Fryback's and Shmueli's utilities (34,35).

Although use of community-weighted utilities in cost-effectiveness analyses is not uncontroversial, it may offer an important complementary perspective in understanding HCV-related outcomes. However, more empirical work is needed to explore differences and similarities between patient-derived utilities and community-weighted utilities for different health states.

This study has highlighted important future quality-of-life research needs in chronic hepatitis C. Patient-elicited utilities for chronic hepatitis C with and without comorbidities and the impact of HCV treatment on utility in longitudinal studies are required to provide important information for evaluation of specific intervention strategies. In the absence of large studies of direct patient-elicited utilities in chronic hepatitis C and considerable variation of expert estimates from alternative utilities, we propose that the community-weighted utilities generated from sizeable SF-36 datasets as derived from Nichol's method in our study can be employed in cost-effectiveness analyses of preventive and therapeutic interventions.

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WHAT IS ACCEPTED AND WHAT THIS RESEARCH ADDS

  • The Panel on Cost-Effectiveness in Health and Medicine has generally advocated the use of community-weighted utilities for cost-effectiveness analysis.
  • At present, cost-effectiveness studies in chronic hepatitis C have employed utilities obtained from surrogates, usually a panel of hepatologists.
  • Application of SF-36 translation methods facilitate use of patient-elicited HRQOL information to generate community-weighted utilities.
  • SF-36 utilities for different stages of liver disease, as derived from this study varied considerably from expert estimates but comparable to direct patient-elicited utilities.
  • In the absence of large studies of patient-elicited utility data in chronic hepatitis C, this study offers a significant contribution toward clinical decision making and cost-effectiveness analyses as it provides a number of estimates of community-weighted utilities derived in a large number of patient populations and clinical contexts.
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References

  1. Dore, GJ, Freeman, AJ, Law, M, et al. Is severe liver disease a common outcome for people with chronic hepatitis C? J Gastroenterol Hepatol 2002;17: 423–430. | Article | PubMed | ISI |
  2. Foster, GR, Goldin, RD, Thomas, HC. Chronic hepatitis C virus infection causes a significant reduction in quality of life in the absence of cirrhosis. Hepatology 1998;27: 209–212.
  3. Bonkovsky, HL, Woolley, JM. Reduction of health-related quality of life in chronic hepatitis C and improvement with interferon therapy. The Consensus Interferon Study Group. Hepatology 1999;29: 264–270.
  4. Ware, JE, Jr, Bayliss, MS, Mannocchia, M, et al. Health-related quality of life in chronic hepatitis C: Impact of disease and treatment response. The Interventional Therapy Group. Hepatology 1999;30: 550–555.
  5. McHutchison, JG, Ware, JE, Jr, Bayliss, MS, et al. The effects of interferon alpha-2b in combination with ribavirin on health related quality of life and work productivity. J Hepatol 2001;34: 140–147.
  6. Miller, ER, Hiller, JE, Shaw, DR. Quality of life in HCV-infection: Lack of association with ALT levels. Aust N Z J Public Health 2001;25: 355–361.
  7. Ware, JE, Jr, Sherbourne, CD. The MOS 36-item short-form health survey (SF-36). I. Conceptual framework and item selection. Med Care 1992;30: 473–483. | Article | PubMed | ISI |
  8. Torrance, GW. Utility approach to measuring health-related quality of life. J Chronic Dis 1987;40: 593–603.
  9. Torrance, GW, Feeny, D. Utilities and quality-adjusted life years. Int J Technol Assess Health Care 1989;5: 559–575. | PubMed | ChemPort |
  10. Kaplan, RM. Quality of life assessment for cost/utility studies in cancer. Cancer Treat Rev 1993;19(Suppl A):85–96.
  11. Torrance, GW, Furlong, W, Feeny, D, et al. Multi-attribute preference functions. Health Utilities Index. Pharmacoeconomics 1995;7: 503–520. | Article | PubMed | ISI | ChemPort |
  12. Drummond, MF. Resource allocation decisions in health care: A role for quality of life assessments? J Chronic Dis 1987;40: 605–619.
  13. Gold, MR, Patrick, DL, Torrance, GW, et al. Identifying and valuing outcomes. In: Gold MR, Siegel JE, Russell LB, Weinstein MC, eds. Cost-effectiveness in health and medicine. Oxford University Press: New York (NY), 1996: 82–134.
  14. Kopec, JA, Schultz, SE, Goel, V, et al. Can the health utilities index measure change? Med Care 2001;39: 562–574.
  15. Mittmann, N, Trakas, K, Risebrough, N, et al. Utility scores for chronic conditions in a community-dwelling population. Pharmacoeconomics 1999;15: 369–376.
  16. Froberg, DG, Kane, RL. Methodology for measuring health-state preferences—II: Scaling methods. J Clin Epidemiol 1989;42: 459–471. | Article | PubMed | ISI | ChemPort |
  17. Feeny, D, Furlong, W, Boyle, M, et al. Multi-attribute health status classification systems. Health Utilities Index. Pharmacoeconomics 1995;7: 490–502. | PubMed | ISI | ChemPort |
  18. Kaplan, RM, Bush, JW, Berry, CC. Health status index: Category rating versus magnitude estimation for measuring levels of well-being. Med Care 1979;17: 501–525.
  19. Krahn, M, Ritvo, P, Irvine, J, et al. Patient and community preferences for outcomes in prostate cancer: Implications for clinical policy. Med Care 2003;41: 153–164. | Article | PubMed | ISI |
  20. Bennett, WG, Inoue, Y, Beck, JR, et al. 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: 855–865.
  21. Dusheiko, GM, Roberts, JA. Treatment of chronic type B and C hepatitis with interferon alfa: An economic appraisal. Hepatology 1995;22: 1863–1873.
  22. Kim, WR, Poterucha, JJ, Hermans, JE, et al. Cost-effectiveness of 6 and 12 months of interferon-alpha therapy for chronic hepatitis C. Ann Intern Med 1997;127: 866–874.
  23. Wong, JB, Bennett, WG, Koff, RS, et al. Pretreatment evaluation of chronic hepatitis C: Risks, benefits, and costs. JAMA 1998;280: 2088–2093.
  24. Shiell, A, Brown, S, Farrell, GC. Hepatitis C: An economic evaluation of extended treatment with interferon. Med J Aust 1999;171: 189–193.
  25. Younossi, ZM, Singer, ME, McHutchison, JG, et al. Cost effectiveness of interferon alpha 2b combined with ribavirin for the treatment of chronic hepatitis C. Hepatology 1999;30: 1318–1324.
  26. Pereira, A, Sanz, C. A model of the health and economic impact of posttransfusion hepatitis C: Application to cost-effectiveness analysis of further expansion of HCV screening protocols. Transfusion 2000;40: 1182–1191.
  27. Sinha, M, Das, A. Cost effectiveness analysis of different strategies of management of chronic hepatitis C infection in children. Pediatr Infect Dis J 2000;19: 23–30.
  28. Singer, ME, Younossi, ZM. Cost effectiveness of screening for hepatitis C virus in asymptomatic, average-risk adults. Am J Med 2001;111: 614–621.
  29. Arguedas, MR, Chen, VK, Eloubeidi, MA, et al. Screening for hepatocellular carcinoma in patients with hepatitis C cirrhosis: A cost-utility analysis. Am J Gastroenterol 2003;98: 679–690. | Article | PubMed |
  30. San Miguel, R, Mar, J, Cabases, JM, et al. Cost-effectiveness analysis of therapeutic strategies for patients with chronic hepatitis C previously not responding to interferon. Aliment Pharmacol Ther 2003;17: 765–773.
  31. Chong, CAKY, Gulamhussein, A, Heathcote, EJ, et al. Health-state utilities and quality of life in hepatitis C patients. Am J Gastroenterol 2003;98: 630–638. | Article | PubMed |
  32. Hornberger, JC, Redelmeier, DA, Petersen, J. Variability among methods to assess patients' well-being and consequent effect on a cost-effectiveness analysis. J Clin Epidemiol 1992;45: 505–512.
  33. Nichol, MB, Sengupta, N, Globe, DR. Evaluating quality-adjusted life years: Estimation of the health utility index (HUI2) from the SF-36. Med Decis Making 2001;21: 105–112.
  34. Fryback, DG, Lawrence, WF, Martin, PA, et al. Predicting Quality of Well-being scores from the SF-36: Results from the Beaver Dam Health Outcomes Study. Med Decis Making 1997;17: 1–9. | PubMed | ISI | ChemPort |
  35. Shmueli, A. The SF-36 profile and health-related quality of life: An interpretative analysis. Qual Life Res 1998;7: 187–195. | PubMed |
  36. Brazier, J, Usherwood, T, Harper, R, et al. Deriving a preference-based single index from the UK SF-36 Health Survey. J Clin Epidemiol 1998;51: 1115–1128. | Article | PubMed | ChemPort |
  37. Lundberg, L, Johannesson, M, Isacson, DG, et al. The relationship between health-state utilities and the SF-12 in a general population. Med Decis Making 1999;19: 128–140.
  38. Brazier, J, Roberts, J, Deverill, M. The estimation of a preference-based measure of health from the SF-36. J Health Econ 2002;21: 271–292. | Article | PubMed | ISI |
  39. Ware, JE, Kosinski, M. SF-36 physical & mental health summary scales: A manual for users of version 1, 2nd ed. Quality Metric Incorporated: Lincoln RI, 2001.
  40. Dolan, P. Modeling valuation for EuroQol health states. Med Care 1997;35: 1095–1108. | Article | PubMed | ISI | ChemPort |
  41. Carithers, RL, Jr, Sugano, D, Bayliss, M. Health assessment for chronic HCV infection: Results of quality of life. Dig Dis Sci 1996;41(12 Suppl):75S–80S.
  42. Hunt, CM, Dominitz, JA, Bute, BP, et al. Effect of interferon-alpha treatment of chronic hepatitis C on health-related quality of life. Dig Dis Sci 1997;42: 2482–2486.
  43. Bayliss, MS, Gandek, B, Bungay, KM, et al. A questionnaire to assess the generic and disease-specific health outcomes of patients with chronic hepatitis C. Qual Life Res 1998;7: 39–55.
  44. Kleinman, L, Zodet, MW, Hakim, Z, et al. Psychometric evaluation of the fatigue severity scale for use in chronic hepatitis C. Qual Life Res 2000;9: 499–508. | Article | PubMed | ChemPort |
  45. Bini, E, Baskies, M, Achkar, J, et al. Impact of HIV infection on health-related quality of life in patients with chronic hepatitis C: An unexpected finding. (abstract, 2001). DDW Conference, Atlanta, Georgia, NATAP Reports.
  46. Hussain, KB, Fontana, RJ, Moyer, CA, et al. Comorbid illness is an important determinant of health-related quality of life in patients with chronic hepatitis C. Am J Gastroenterol 2001;96(9 Suppl):2737–2744.
  47. Kramer, L, Bauer, E, Funk, G, et al. Subclinical impairment of brain function in chronic hepatitis C infection. J Hepatol 2002;37: 349–354.
  48. Gallegos-Orozco, JF, Fuentes, AP, Argueta, JG, et al. Health-related quality of life and depression in patients with chronic hepatitis C. Arch Med Res 2003;34: 124–129.
  49. Rasenack, J, Zeuzem, S, Feinman, SV, et al. Peginterferon alpha-2a (40kD) [Pegasys®] improves HR-QOL outcomes compared with unmodified interferon alpha-2a [Roferon®-A] in patients with chronic hepatitis C. Pharmacoeconomics 2003;21: 341–349. | Article | PubMed | ChemPort |
  50. Cordoba, J, Flavia, M, Jacas, C, et al. Quality of life and cognitive function in hepatitis C at different stages of liver disease. J Hepatol 2003;39: 231–238. | Article | PubMed |
  51. Neary, MP, Cort, S, Bayliss, MS, et al. Sustained virologic response is associated with improved health-related quality of life in relapsed chronic hepatitis C patients. Semin Liver Dis 1999;19(Suppl 1): 77–85.
  52. Fontana, RJ, Moyer, CA., Sonnad, S, et al. Comorbidities and quality of life in patients with interferon-refractory chronic hepatitis C. Am J Gastroenterol 2001;96: 170–178.
  53. Rodger, AJ, Jolley, D, Thompson, SC, et al. The impact of diagnosis of hepatitis C virus on quality of life. Hepatology 1999;30: 1299–1301.
  54. Ishak, K, Baptista, A, Bianchi, L, et al. Histological grading and staging of chronic hepatitis. J Hepatol 1995;22: 696–699. | Article | PubMed | ISI | ChemPort |
  55. Picton, TW. The P300 wave of the human event-related potential. Clin Neurophysiol 1992;9: 456–479.
  56. Fryback, DG, Dasbach, EJ, Klein, R, et al. The Beaver Dam Health Outcomes Study: Initial catalog of health-state quality factors. Med Decis Making 1993;13: 89–102. | PubMed |
  57. Salomon, JA, Weinstein, MC, Hammitt, JK, et al. Cost-effectiveness of treatment for chronic hepatitis C infection in an evolving patient population. JAMA 2003;290: 228–237.
  58. Chapman, RH, Berger, M, Weinstein, MC, et al. When does quality-adjusting life-years matter in cost-effectiveness analysis? Health Econ 2004;13: 429–436. | Article | PubMed |
  59. Pol, S, Carnot, F, Nalpas, B, et al. Reversibility of hepatitis C virus-related cirrhosis. Hum Pathol 2004;35: 107–112.
  60. Revicki, DA, Kaplan, RM. Relationship between psychometric and utility-based approaches to the measurement of health-related quality of life. Qual Life Res 1993;2: 477–487.
  61. Hollingworth, W, Deyo, RA, Sullivan, SD, et al. The practicality and validity of directly elicited and SF-36 derived health state preferences in patients with low back pain. Health Econ 2002;11: 71–85.
  62. Feeny, D, Furlong, W, Saigal, S, et al. Comparing directly measured standard gamble scores to HUI2 and HUI3 utility scores: Group- and individual-level comparisons. Soc Sci Med 2004;58: 799–809.
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Acknowledgements

The National Centre in HIV Epidemiology and Clinical Research is funded by the Australian Government Department of Health and Ageing, and is affiliated to the Faculty of Medicine, The University of New South Wales.

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