Estimation of an EORTC QLU-C10 Value Set for Spain Using a Discrete Choice Experiment

Cost-utility analysis (CUA) is often used to inform whether new treatments or interventions should be reimbursed within a healthcare system. CUA typically measures benefits in terms of quality-adjusted life-years (QALYs), combining the length of life with an index of health-related quality of life (HRQoL), often known as a utility, or values, of patients. Values can be obtained using a variety of direct and indirect methods, including using description of health states associated with a condition (i.e. vignettes), asking patients to value their own health directly or, more commonly, indirect valuation using a preference-based measure (PBM) of health [1].

PBMs comprise a descriptive system through which health is described, and a value set reflecting strength of preference of members of the general public, or more rarely patients, for the health state described. PBMs can be generic (GPBMs) or condition-specific. While GPBMs can be used to describe health problems relevant across multiple diseases and conditions, condition-specific PBMs typically describe health problems occurring in a specific condition of interest [1‐3], often focussing on particular symptoms, aspects of functioning or side effects.

For use in CUA, prominent health technology assessment bodies such as the National Institute for Health and Care Excellence (NICE) in the UK and the Zorginstituut in the Netherlands express a preference for using GPBMs [4, 5]. In other countries, for example in Spain, the choice of which measure to use for obtaining values for QALYs calculation is left to the analyst, who can consider either GPBM or condition-specific PBM [6]. There are multiple arguments supporting the use of GPBM, including the fact that GPBMs detect the negative impact of a wide range of comorbidities alongside the positive impact of interventions, as well as avoiding labelling and focusing effects biases [2]. More importantly, using a single GPBM for all assessments across all conditions allows for cross-program comparability [7]. The potential concern with using a single GPBM alone is that the chosen GPBM may not be valid and responsive for the group of patients being examined. In those cases, using a different GPBM or condition-specific PBM is necessary [5].

A limited number of GPBMs dominate the literature [8], specifically the EQ-5D, the SF-6D, the Health Utility Index mark 3 (HUI3), the Assessment of Quality of Life (AQoL),the 15 Dimensions (15D) and the Quality of Wellbeing Self-Administered (QWB-SA) [9, 10]. Although there is considerable evidence for their validity and responsiveness in many common medical problems such as skin, respiratory, genitourinary, endocrine, nutritional and metabolic diseases [7, 11, 12], there is also mixed or inconsistent evidence for all these measures in some specific types of cancers [7, 11, 13].

Recently, a partnership between the Multi-Attribute Utility in Cancer (MAUCa) Consortium and the European Organisation for Research and Treatment of Cancer (EORTC) Quality of Life Group led to the development of the EORTC QLU-C10D [14]. The EORTC QLU-C10D is a condition-specific PBM derived from the EORTC QLQ-C30, one of the most commonly used patient-reported outcome measures in cancer randomized controlled trials [15]. Given the widespread use of the EORTC QLQ-C30, the EORTC QLU-C10D might be an alternative when the preferred GPBM is not included in the trial of interest, or a useful measure to perform CUA sensitivity analysis in those cases in which the preferred GPBM reports mixed validity and responsiveness results. This is because the QLU-C10D allows values to be directly estimated from clinical studies that have used the QLQ-C30, without the need for mapping onto GPBMs or additional data collection.

The development of the EORTC QLU-C10D followed two stages. First, the health state classification system was developed to reduce the thirty items of the EORTC QLQ-C30 into 13 items covering ten dimensions [16]. Subsequently, a valuation method using a discrete choice experiment (DCE) was developed [17], which was then used to generate a value set from members of the general public of Australia [18]. This valuation method is valid and has been increasingly used due to its ease of application and reduction in data collection burden [19, 20].

For the conduct of CUAs, it is recommended to use country-specific value sets, as differences in preferences across countries might substantially alter the values obtained [21‐23] and consequently cost-effectiveness estimates. Hence, valuation studies for the EORTC QLU-C10D have been completed, or are currently being undertaken, in numerous countries, including Canada [24], Germany [14], the United Kingdom [25], Austria, France, Italy, Poland [26], the Netherlands, the United States [14] and Singapore [27]. The current study reports on the EORTC QLU-C10D valuation in Spain. Preferences were elicited from a representative sample of the Spanish general population, replicating the methods employed by King and colleagues [18].

This study collected preferences for a set of EORTC QLU-C10D health states to derive a value set representative of the Spanish adult population. For all attributes, statistically significant decrements were associated with the worst level (level 4), reflecting that people have a strong preference to not live with this degree of dysfunction or symptom severity. For five of the attributes, namely physical functioning, fatigue, lack of appetite, nausea and bowel problems, decrements were statistically significant for all levels, while for four attributes, namely role, social and emotional functioning and pain, decrements were statistically significant for level 3 and 4 but not for level 2. Lack of monotonicity occurred in three attributes, but this was adjusted through collapsing the two dimensions levels and re-estimating the model. The monotonically ordered generalized estimating equation model represents a consistent tariff that can be used for the derivation of health state values to use in economic evaluations as a second-best option when the preferred GPBM is not included in the trial of interest, or as a useful alternative to performing CUA sensitivity analyses.

The rank order of QLU-C10 items found in this study is in line with those of previous QLU-C10 valuations, with generic items generally receiving larger weights compared with cancer-specific items. Among the generic items, physical functioning reported the largest decrements followed by pain and role functioning. These findings are in line with those in Germany [14], Austria, Italy, Poland [26] and France [39]. Among the cancer-specific items, nausea reported the largest utility decrement followed by bowel problems, which mimicked the same rank order found in the Austrian valuation study [16].

In the current study, weights for three items did not decrease monotonically. Lack of monotonicity for some of the QLU-C10 items was observed also in other valuation studies, for example those of Austria, Australia and the Netherlands, as well as in numerous valuations of other PBMs (e.g. [40‐43]). There are multiple possible explanations for the observed inconsistencies, such as, among the others, the size of the descriptive system (i.e. number of attributes) in relation to the sample of the study (i.e. number of participants), the wording of the attributes (i.e. responders’ understanding of the descriptors and labels), the administration mode (i.e. engagement of participants in online surveys) or the fact that those items do not have a stable impact on utility. While the current study accounted for the lack of monotonicity by collapsing levels 3 and 4, therefore generating a consistent tariff, the reasons underlining the observed inconsistencies are of interest and should be investigated in future research.

Two of the three cancer-specific items, fatigue and lack of appetite, were associated with small weights, a result that was in line with the attribute decrements of the QLU-C10 found in Australia and Germany [14, 18] and with preferences registered in other PBM experimental studies [44]. A possible explanation for this might be that members of the general public do not have experience of cancer symptoms, and how these severely impact the HRQoL of patients [45‐47], or may have some experience of those symptoms, but at a lesser degree of intensity. Valuation studies eliciting values from cancer patients are currently ongoing. Results of these studies will inform on whether the small weights associated with cancer attributes depend on the population performing the valuation task or the comparative relevance of these attributes in explaining HRQoL against generic attributes.

In the current study, two different modelling approaches were employed, namely generalized estimating equation and mixed logit. The models reported similar mean utility decrements. We chose to estimate the QLU-C10 tariff using a generalized estimating equation, as in economic evaluation mean responses are more important than variability in preferences between different groups (i.e. preference heterogeneity) [18].

The current study developed a value set for the QLU-C10, a condition-specific PBM. Value sets for a number of other condition-specific PBMs exist, including those for the Amyotrophic Lateral Sclerosis utility index (ALS) [48], the Health Assessment Questionnaire (HAQ) [49], the Asthma Quality of Life Questionnaire (AQLQ) [50], the Exacerbation Utility (Exact U) [51], the Multi Sclerosis Impact Scale (MSIS) [49], the NEWQoL 8D [41] and the Dementia Quality of Life measure (DEMQoL) [42]. Valuation studies of condition-specific PBMs differ substantially in terms of the chosen preference elicitation technique (discrete choice experiment, time trade-off, standard gamble, rating scale etc.), the population providing preference weights (general public, patients, professionals etc.) and the country in which preference weights were obtained [52]. These choices have an impact on the relevance of the value sets for different application contexts. For example, a value set obtained from general public responders may be preferred over a value set obtained from patients by HTA bodies that take the payer perspective, while patients’ index scores may be better suited to investigations of large patient registries, population health studies and for personalized medicine [e.g. 53]. The Spanish value set for the QLU-C10 was developed to facilitate the conduct of economic evaluation in Spain, where condition-specific PBM values are accepted and used [6].

This study has some limitations. First, it used an online administration procedure. While this has the advantage of being cheaper and less time consuming than face-to-face administration, it may be associated with poorer quality of data due to responders’ reduced engagement with the task and strategic behaviours [54‐56]. To assess whether this occurred, time stamps were investigated, without finding any relevant problem. Yet, this possibility cannot be entirely ruled out. Second, the colour coding approach used a yellow colour for the non-overlapping items. Currently, there is a debate in the DCE literature with some supporting this approach, and others championing intensity colour coding as a better alternative [57]. While the approach adopted for task presentation is reasonable, it is important to acknowledge that other colour coding variants might have been used. Third, the sample reported higher education levels than the Spanish general population. From a theoretical point of view, this may have an impact on the values obtained, for example because of different understanding of the task, different priorities and different underlying health of responders with varying education levels. Yet, departures in the sample representativeness such as those reported in this study are similar to the ones of other valuation studies [58]. Moreover, there is evidence that educational levels do not substantially impact values when elicited through time trade-off [59, 60], albeit this evidence is not available for DCE. Fourth, in the DCE, the physical functioning domain was presented as two attributes (related to short walk and long walk) to simplify the wording of the attribute. However, the underlying design treated it as a single attribute. This may have had an impact on the values obtained, resulting in a larger decrement associated with physical functioning. Despite these limitations, this study also has important strengths. It generated the first value set for the QLU-C10 based on preferences elicited from the Spanish population. The availability of a value set for this population allows for a more theoretically sound alternative to the use of mapping techniques from non-PBMs to PBMs and from PBMs to non-PBMs [24]. It also allows easy access to values for end users, researchers and policy makers. Furthermore, by being part of a broader European programme of research, the current study allows comparative assessments of preferences for health states relevant to cancer populations across European countries. Finally, the methods and the DCE design employed in the current research have been previously tested in an experimental study [17] and have been already used for the estimation of QLU-C10 preference weights in different European countries, which increases the confidence in the results obtained.

This study generated a QLU-C10 tariff for Spain, which can be used in future economic evaluations or for comparative research across cancer populations in Europe.