The FACT-8D, a new cancer-specific utility algorithm based on the Functional Assessment of Cancer Therapies-General (FACT-G): a Canadian valuation study

The cost of cancer therapies is constantly rising. Understanding the financial implications of these therapies on the healthcare system is important, but it is also important to understand the impact these therapies have on patients in order to assess their value. As such, there is a need for sensitive instruments that capture the different dimensions of health-related quality of life (HRQL) to reflect both the therapeutic benefit and the treatment burden experienced by patients with cancer. Ideally, responses to these instruments would provide a utility to inform cost-utility analysis (CUA), the preferred economic evaluation approach by many health technology assessment organizations [1‐3]. Conventionally, due to their ease in administration, “off the shelf” generic multi-attribute utility instruments (MAUIs) (e.g., the EQ-5D [4, 5] or the Health Utility Index (HUI) [6, 7]) are used to generate utilities, which are then suitable for estimating QALYs. Utilities are measured on a scale, where one represents full health, 0 represents states deemed to be as bad as being dead, and negative values represent states deemed to be worse than being dead. However, by their nature, the dimensions of generic instruments cover ubiquitous aspects of health, but may be limited in their ability to capture specific impacts of particular conditions and treatments.

The Functional Assessment of Cancer Therapies-General (FACT-G) [8], a widely used cancer-specific patient-reported outcome (PRO) measure, is used often to generate HRQL endpoints in cancer clinical trials. It is a stand-alone HRQL profile measure, and is also included in the many other condition and treatment-specific questionnaires in the FACIT measurement suite [9]. The FACT-G enables patients to self-report the impact of their cancer and treatments, but the scores it yields cannot be used in CUA because they do not provide any information about the strength of individuals’ preferences for specific HRQL dimensions of the instrument or about the trade-off between HRQL and survival. Responses on the FACT-G serve as a description rather than a valuation of health states; as such, their outputs are not health state utilities.

One approach that enables FACT-G responses to be measured onto a utility scale is regressing utility scores from generic measures onto the FACT–G total score or its dimension subscores using datasets containing both measures. However, a review of cancer mapping studies indicated that these regressions generally show poor goodness-of-fit between the observed generic utilities and the mapped cancer-specific utilities [10]. Another approach is to derive a MAUI to generate utilities from data collected from the FACT-G. A disadvantage of this approach include the inability to compare results across programs designed for different conditions and the potential neglect of co-morbidities; however, cancer-specific MAUI ensure that the resulting utilities better reflect the impact of cancer when incorporated into a CUA. Further, having a cancer-specific MAUI based on the FACT-G would make better use of studies where the FACT-G has generated HRQL endpoints but generic MAUIs have not been included.

The Multi-Attribute Utility in Cancer (MAUCa) Consortium has derived a MAUI to generate utilities from data collected from the QLQ-C30 to produce the European Organization of Research and Treatment in Cancer (EORTC) QLU-C10D [11]. Since its development, a number of country-specific utility weights for the QLU-C10D have followed, including Canada [12‐19]. Recently, the MAUCa Consortium derived the health state classification system from items of the FACT-G and the Australian value set for the new FACT-8D has now been produced [20]. However, there is a need to inform cancer priority setting and resource allocation decisions in other jurisdictions; as such, country-specific utility weights for the FACT-8D are needed. The aim of this current paper is to produce the FACT-8D value set for the Canadian general population as the Canadian Agency for Drugs and Technologies in Health (CADTH) recommends that the preferences of the Canadian general population should be the reference case [1].

Building on the work previously reported in the FACT-8D health state classification system development and Australian valuation study [20], we determined the Canadian value set for the FACT-8D, a cancer-specific algorithm for calculating utilities. The Canadian FACT-8D value set provides another decision-making tool to inform CUA in Canada, especially in situations where only the FACT-G was administered to assess patients’ HRQL. By using select item responses of the FACT-G, FACT-8D utilities can be generated which, in turn, can inform CUA.

The Canadian valuation results revealed that the main contributors of the general population respondents’ utility were pain, nausea, problems with working, and problems with support from family and/or friends; these findings were similar to those reported in the Australian valuation study [20]. Also captured in the FACT-8D classification system were dimensions reflecting other symptoms and impacts of cancer and its treatment (e.g., fatigue, sleep problems, and worry about future health). With the exception of pain and nausea, the utility decrements for the dimensions considered more cancer-sensitive were generally smaller. However, we do not anticipate this observation will reduce the impact of cancer-sensitive dimensions when the FACT-8D utilities inform CUA as other factors will affect the prevalence of the cancer-sensitive dimensions and the difference in symptom prevalence between trial arms or other comparator groups. The extent to which the inclusion of cancer-sensitive dimensions provides a more relevant and sensitive cancer-specific utility measure will depend upon the clinical context.

The Canadian general population’s valuation of the health states defined by the FACT-8D was generally monotonic within each dimension, such that poorer HRQL levels had larger utility decrements. However, when comparing the Canadian valuation results with those of Australia, both countries’ value sets revealed some slightly inconsistent orderings of utility decrements across the levels of most dimensions. Only nausea and one other dimension demonstrated no inconsistency: fatigue for Canada and pain for Australia. Sleep was problematic in both countries’ valuations. To overcome the inconsistency for this dimension, the five levels were collapsed down to two levels to capture minor and severe sleep issues. Collapsing of levels to reflect minor and severe issues was also observed for the sadness and worry dimensions. For the remaining dimensions describing pain, problems with work, problems with support with family and friends, inconsistencies were observed in the less severe levels.

The worst possible state (i.e., PITS state) defined by Canadian FACT-8D algorithm is − 0.65, which is similar in magnitude to the Australian PITS state (− 0.54). However, when compared to other MAUIs, the Canadian FACT-8D PITS state is significantly lower than that of the EQ-5D-5L (− 0.15) [30] and the cancer-specific utility instrument, QLU-C10D (− 0.15) [12]. While the difference between the PITS values of the FACT-8D and the EQ-5D-5L may be result of different valuation methods used (e.g., FACT-8D used the DCE whereas EQ-5D-5L used TTO), comparing the PITS values for the FACT-8D and the QLU-C10D offers a more informative comparison. Assessing the common dimensions between the two cancer-specific algorithms reveals that most severe levels of pain, fatigue, and nausea demonstrates greater disutilities for the FACT-8D. This may be a result of the extra response level in the FACT-8D but this will need to be explored in the future.

The conditional logit was selected over the mixed logit results because economic evaluation is mostly concerned with the mean response; preference heterogeneity is a secondary concern. The choice of a monotonic main-effects model for calculating utility is readily accessible for a range of end users, clinical interpretable and consistent with the FACIT quality of life conceptual model.

The FACT-8D health state classification was valued using a DCE. This approach has a strong theoretical measurement framework, well established statistically robust experimental design and modelling methods, and demonstrated feasibility with online recruitment and data collection [29, 31, 32]. In this study, respondents appraised choice sets containing nine attributes. The relatively larger number of attributes raised concerns regarding the cognitive burden of the respondents; however, we conducted innovative work to have only four attributes differ in each choice set. Although previous work revealed that the respondents preferred format of yellow highlighting [22], the concerns of respondents employing heuristics, such as considering a single attribute, to trade-off between other attributes, were alleviated when the majority of the respondents considered most or all of the attributes presented to them.

There are limitations associated with this study. While the valuation survey sample consisted of a large number of respondents with quota sample sampling achieving population representativeness for age, sex, and province/territory of residence, the study sample tended to have higher education and report poorer health. To overcome this limitation, we raked the study sample on under represented characteristics.

The influence of sociodemographic variables to the utility estimates will be assessed in future analysis of pooled data from international valuations of the FACT-8D. While it may seem like having general population respondents valuing these health states is a limitation due to their inexperience with cancer, it is important to adopt an extra-welfarist approach as a publicly-funded healthcare system exists in Canada. As such, the preferences from general population respondents should maximize societal health. The health state descriptions make no mention of “cancer”, alleviating any potential stereotypes respondents may have in regards to a cancer diagnosis; although previous research has revealed that disease labels do not affect health state valuations [33]. Further, due to a logistical oversight during survey implementation, individuals on devices with small screen sizes (e.g., cellphones, tablets) were initially allowed into the survey but then encountered a system initiated timeout that prevent them from proceeding to the DCE component; as such, the reported rate of participant involvement is not accurate due to the device used.

We used the FACT-8D valuation methods developed by King et al. [20]. This involved reversing the direction of the phrasing for the three positively worded FACT-G dimensions (i.e., work, sleep, and support), a solution that solved initial problems in the patterns of utility decrements in the developmental work conducted in Australia [20]. While reframing the positively worded dimensions as problem statements in the DCE reduced the cognitive burden placed on the respondents when completing the choice tasks, this posed the question of how to map the corresponding positively framed FACT-G responses to utility decrements in the FACT-8D utility scoring algorithm. The members of the MAUCa Consortium propose mapping to the original FACT-G item wording by reverse scoring when calculating utility decrements, as shown in Table 1 and the scoring instructions in the Additional file 1. We acknowledge that when used as self-reported health items, negatively and positively framed versions of the work, sleep, and support items would not necessarily yield mirror image results. However, this solution is pragmatic in allowing utility values to be generated from existing FACT-G datasets, with the reframing used solely for the purpose of making the DCE valuation task more feasible for participants. Better solutions may be found in future research, particularly if similar problems arise in other valuation DCEs with positively and negatively framed items. The measurement properties of the FACT-8D (using the Australian value set) has been tested against the EQ-5D-5L (scored using the UK 3L crosswalk and the 5L England value set) [34]. The FACT-8D demonstrated good convergent validity and responsiveness but the EQ-5D-5L showed better known groups’ validity.

We did not test potential interactions between pairs of FACT-8D dimensions although we acknowledge that they may exist. The influence of potential interactions could be explored in the future both quantitatively and qualitatively. We opted for a more parsimonious approach as testing all possible interactions would require an unfeasibly large sample size due to the many additional coefficients that would need to be estimated from the DCE data. The model presented in the article excluded complex interactions and therefore, is clinically interpretable and make it comprehensible to end-users.

In our work, we anchored the utilities on the standard QALY scale using a common approach [29]. The resulting Canadian FACT-8D value set assumes the zero condition (i.e., a health state of zero duration is equivalent to the dead state). However, previous work has revealed that different DCE-based approaches to anchor utility scores can have varying impact on the generated utilities [35]. This may be a limitation when value sets determined by DCEs are used to guide resource allocation decisions. While it is possible to anchor utilities by including dead as a health state within the DCE, this is problematic within a random utility theory framework as some respondents may never acknowledge a health state to be less preferred than immediate death [36].

Results from the study add to the continuing debate amongst health economists regarding the use of generic versus cancer-specific utility instruments in informing resource allocation decisions. The FACT-8D contains a large number of dimensions specific to cancer. While the FACT-G dimensions are more sensitive in capturing cancer patients’ HRQL, the resulting metrics make it difficult to compare to CUA results in other therapeutic areas. Future work is needed on accessing the acceptability of cancer-specific utility instruments in informing resource allocation decisions.

The largest impacts on utility included three generic dimensions (i.e., pain, support, and work) and nausea, a symptom caused by cancer (e.g., brain tumours, gastrointestinal tumours, malignant bowel obstruction) and by common treatments (e.g., chemotherapy, radiotherapy, opioid analgesics). Our findings demonstrate that cancer-specific utilities can be determined using responses to the FACT-G (as well as many FACIT measures that embed the FACT-G items); this, in turn, facilitates CUA for cancer interventions from a Canadian perspective. The widespread use of the FACT-G to measure quality of life outcomes of cancer patients will enable utilities not only to be estimated prospectively but also from a large number of retrospective studies. While the results reveal that the Canadian value set for the FACT-8D is similar to the Australian value set [20], CADTH recommends that preferences of the Canadian general population should be the reference case to guide societal decisions [1]. The Canadian FACT-8D value set affords cancer-specific utility weights that may be more sensitive to differences resulting from cancer care than a generic MAUI, which may be more informative in guiding cancer priority setting and resource allocation decisions in Canada. We intend to conduct head-to-head comparisons of the FACT-8D versus generic MAUIs assess its performance. The availability of more QALY estimates and CUAs will enable decision makers to be more informed when allocating resources in Canada’s publicly funded health care system.