Australian Utility Weights for the EORTC QLU-C10D, a Multi-Attribute Utility Instrument Derived from the Cancer-Specific Quality of Life Questionnaire, EORTC QLQ-C30

Economic evaluation is central to the evaluation of new therapies and technologies in many countries. Cost-utility analysis (CUA) is a form of economic evaluation that quantifies health outcomes on a standardised metric, typically the quality-adjusted life-year (QALY). The quality adjustment is provided by a ‘value set’; that is, a set of utility weights for a range of possible health states within any given health state classification system. Value sets can be derived for classification systems originally developed as multi-attribute utility instruments (MAUI), or by adaptation of existing health-related quality of life (HRQoL) profile measures [1, 2]. Such a measure, the EORTC QLQ-C30, is a widely used core questionnaire in the modular HRQoL suite of the European Organisation for Research and Treatment of Cancer (EORTC) [3].

The Multi-Attribute Utility in Cancer (MAUCa) Consortium aims to facilitate the use of HRQoL data in CUA in cancer settings by providing a series of country-specific value sets for the QLQ-C30. To this end, we have developed a health state classification system containing 13 of the QLQ-C30’s 30 items, combined into ten dimensions (Table 1) [4] and a valuation method based on a discrete choice experiment (DCE) [5]. These are key components of the QLU-C10D, a new cancer-specific MAUI. The aim of this current paper is to apply the valuation method in an Australian general population sample to produce the first country-specific utility weights for the QLU-C10D.

This paper reports the first value set for the QLU-C10D, a MAUI derived from the EORTC QLQ-C30. This approach has two important advantages. First, it allows direct quantification of utility for use in economic evaluation from responses to the QLQ-C30, a widely used cancer-specific HRQoL questionnaire. Second, it captures dimensions related to cancer symptoms that are not included in generic instruments (particularly appetite, nausea and bowel problems). The main drivers of utility were the generic dimensions, with the largest utility decrements for physical, role, social and emotion functioning and pain, mirroring generic MAUIs. However, sizeable decrements were associated with cancer-sensitive dimensions, particularly nausea, bowel problems and appetite. Problems with sleep and fatigue were smaller, perhaps because minor problems with sleep and fatigue are relatively common and therefore considered less important by survey respondents. It is possible that the size of relative utility weights would differ for cancer patients with experience of extreme levels of fatigue and sleep disturbance. The MAUCa Consortium is exploring this question in a related study currently underway, sampling Austrian patients and general population. Even though the utility decrements for the cancer-sensitive dimensions were smaller than those for the more generic dimensions, their inclusion provides a more relevant measure of utility for cancer interventions.

Physical functioning had larger utility decrements than other dimensions, for each level. In the QLU-C10D, physical functioning is represented by walking, making it somewhat comparable to the EQ-5D Mobility and HUI3 Ambulation dimensions. In the HUI3 multi-attribute utility function, Ambulation does not have the largest utility decrements for any level [32]. Results from EQ-5D valuation studies are mixed. For example, in Australian valuations of the EQ-5D(3L) using DCE, Mobility had the largest utility decrement of all dimensions at level 3 but not level 2 [33], while in Australian EQ-5D(3L) valuations using time trade-off (TTO), Anxiety/Depression and Pain/Discomfort had the largest utility decrements at level 3, and at level 2 Mobility had the second lowest utility decrement [34]. Why might ability to walk have the largest impact on utility in the current study? First, physical functioning appeared as the first dimension in the choice set, as in the QLQ-C30 parent questionnaire. However, we have previously investigated and dismissed order effect after randomised testing in DCEs for both QLU-C10D [11] and EQ-5D-5L [35]. Second, due to the complexity of the level descriptors, physical functioning appeared in the DCE as two attributes, even though it was a single four-level dimension in the DCE experimental design and analysis. A useful comparator here is the EORTC-8D, where Physical Functioning was presented as a single five-level dimension (representing the same QLQ-C30 long/short walk items as in the QLU-C10D) [27]. It had the third largest utility decrement for the worst level (exceeded by Social and Emotional Functioning) and the second largest for level 2 (exceed by Pain). While country is a confounder of this comparison, this issue cannot be ruled out as a driver of the effect, but will be resolved soon as UK valuations of the QLU-C10D are underway. Finally, the QLU-C10D Physical Functioning dimension covers a large range of mobility with four levels reflecting the combined range of the two QLQ-C30 walking items (see Table 1), while other dimensions are based on the range in only one item. It may therefore be appropriate that the utility decrements are correspondingly large.

In similar studies, initial models have contained some inconsistent orderings of utility decrements within dimensions, particularly for dimensions with small utility impacts [24‐29]. Consistent with previous studies, we imposed constraints to remove non-monotonicities. This did not reduce model fit markedly, and avoids perverse results in QALY calculations.

Anchors at one (full health) and zero (death) are imposed by the QALY model, but there is no natural anchor for the pits state (worst possible health state). The QLU-C10D pits state value is -0.095. This is considerably lower than the 0.29 pits state value for the QLU-C10D’s precursor, EORTC-8D [27], which has eight of the ten QLU-C10D dimensions (four with exactly the same items and levels as the QLU-C10D, four that differ slightly), but lacks Sleep and Appetite. In our study, the worst levels of Sleep and Appetite had a combined utility decrement of 0.09, so the difference in content explains some of the difference in pits state values. Since the EORTC-8D was valued with TTO in the United Kingdom (UK) general population, valuation method and country likely explain much of the remainder, as both instruments share a simple additive utility function. The values of the pits state in the original UK and Australian EQ-5D(3L) TTO studies were −0.594 [36] and −0.217 [34], respectively, and in the Australian EQ-5D(3L) DCE study it was −0.516 [33]. Variations in the value of health states, including the pits state, are driven by several factors [37], including country-specific cultural differences in attitudes to trading between mortality and morbidity, different health state classification system content, valuation method and utility functional form. Arguably, a lower pits state value means a greater range in a value set which may lead to greater differences between interventions in CUA. A related issue is sensitivity to mild impairments. Values for health states with level 2 across all dimensions were 0.464 for the QLU-C10D (Australian DCE) and 0.715 for the EORTC-8D (UK TTO). Assessing the sensitivity of the QLU-C10D to differences in mild and extreme QOL impacts, and comparing this with other candidate MAUIs, are important issues for future research.

The DCE method has emerged as an alternative to TTO and standard gamble (SG) methods for valuation of health outcomes in the past decade, and has now been used in a number of studies [6, 20, 21, 33, 38, 39]. The discrete choice method is attractive for several reasons: it is embedded in a strong theoretical measurement framework; it utilises well established statistically robust experimental design and modelling methods; it is based on a relatively simple judgmental task; it is feasible with online recruitment and data collection. The use of DCEs to value health states is maturing, but still presents some challenges. While the judgmental task is simple relative to TTO and SG, thus allowing survey respondents to consider a larger number of attributes, the 12 attributes in the current study is a relatively large number. This study confirms the QLU-C10D valuation methods experiment in finding this is feasible for respondents [5]. We reduced cognitive challenge firstly by allowing only four dimensions to differ in each choice set and secondly by presenting choice sets in the format preferred by participants in the methods experiment, using yellow highlighting to identify differences between situations A and B [5]. Allowing only some dimensions to differ across choice sets has the additional advantage of requiring respondents who employ heuristics such as considering a single attribute to trade off between other attributes. We designed an experiment with 960 choice sets that would maximise statistical efficiency in estimating utility parameters. This meant the survey included some health states that might seem rather unlikely to respondents, such as severe vomiting yet no problems with social function. However, we note that in the patient-reported QLQ-C30 data used to derive the QLU-C10D health state classification system, at least one patient reported each pairwise combination of levels [4].

We used two modelling approaches, conditional logit and mixed logit, which yielded similar mean utility decrements. We have chosen conditional logit (Model 2) as the basis for calculating utilities for CUA for the following reasons: (i) for economic evaluation, we are generally most interested in the mean response, so preference heterogeneity is a secondary concern; (ii) to our knowledge, there remains uncertainty about the appropriate distributional assumptions for the mixed logit.

This study has several strengths and some limitations. It provides a preference-based measure for calculating utilities for the QLQ-C30, which is theoretically and empirically stronger than using mappings of the EORTC QLQ-C30 to other preference-based utility measures [40]. The development of the health state classification system was psychometrically thorough [4]. The valuation survey sample was large, with quota sampling achieving population representativeness for age and sex. The extent to which non-representativeness on the other measured sociodemographic variables is a limitation is as yet unknown, and will be explored in future researching by pooling valuation data across the MAUCa Consortium. We established the feasibility of our DCE method [5], and have noted its strengths and limitations above. We used modelling approaches appropriate to our data structure and analysis purpose. Our choice of a monotonic main-effects model for calculating utility is readily accessible for a range of end users, clinically interpretable and consistent with the EORTC quality-of-life conceptual model.

The appropriateness of disease-specific utility weights for CUA is debated by health economists [41]. Conventionally, generic MAUIs such as the EQ-5D are used, primarily to enable comparability across health conditions and interventions. However, the capacity of generic instruments to capture clinically relevant differences in cancer is also debated [42‐44]. Arguably, the QLU-C10D should provide a more cancer-sensitive measure of utility than provided by generic MAUIs, although this is yet to be tested empirically. Further, data on generic utility measures may not always be available. The QLU-C10D enables utility values to be retrospectively generated from the wealth of existing QLQ-C30 data, thus facilitating economic evaluation from existing studies. It is anticipated that the QLU-C10D will have good psychometric properties, and future research will examine this, as well as assessing its performance relative to generic MAUIs.

The QLU-C10D has been developed by the MAUCa Consortium in collaboration with the EORTC QOL Group. A key strength of the Consortium’s approach is the use of identical valuation methods across countries, creating a unique opportunity to explore predictors of health outcome values, including country, age, sex, education and health status of valuation survey respondents—this will be done in future analyses.

The QLU-C10D is endorsed by the EORTC QOL Group, and supersedes the EORTC-8D. Notably, the development of the health state classification system of the EORTC-8D was based on data from 655 multiple myeloma patients, while that of the QLU-C10D was informed by a much larger (n = 2616) and more diverse sample: 13 countries, 15 primary cancer types, localised/regional (n = 1037) and recurrent/metastatic stages (n = 1579) [4]. The EORTC QOL Group now has stewardship of the QLU-C10D, being responsible for all aspects of its management, developing and maintaining information regarding administration, scoring and interpretation, and housing relevant materials on the EORTC QOL website. This will make the QLU-C10D widely available for use prospectively and retrospectively, and thereby facilitate the incorporation of quality of life into healthcare decision-making for cancer care.

CUA represents a major part of the reimbursement process in many countries. In Australia, the government guidelines for preparing submissions to the Pharmaceutical Benefits Advisory Committee (PBAC) favour direct estimation of utilities over mapping, do not mandate a particular MAUI but prefer Australian-based preference weights and encourage the use of patient-reported outcomes/MAUIs that capture all important disease- or condition-specific factors [45; pages 37, 77]. Based on the experience of RV and RN serving on PBAC and its subcommittees, submissions for cancer interventions frequently present QLQ-C30 data. Therefore, the value set presented here will aid Australian resource allocation decisions. Further, the methods presented in this paper provide a template for further international valuations of the QLU-C10D. A number of these are underway, using exactly the same DCE design, presentation format and analysis, including Austria, Canada, France, Germany, Poland, the UK and the US, enabling assessment of international comparability of preferences for cancer-specific health states.