Deriving a Preference-Based Measure for Myelofibrosis from the EORTC QLQ-C30 and the MF-SAF

A scoping review to create a framework for the steps in developing condition-specific preference-based instruments de novo or from an existing non-preference-based instrument: use of item response theory or Rasch analysis

BACKGROUND

There is no widely accepted framework to guide the development of condition-specific preference-based instruments (CSPBIs) that includes both de novo and from existing non-preference-based instruments. The purpose of this study was to address this gap by reviewing the published literature on CSPBIs, with particular attention to the application of item response theory (IRT) and Rasch analysis in their development.

METHODS

A scoping review of the literature covering the concepts of all phases of CSPBI development and evaluation was performed from MEDLINE, Embase, PsychInfo, CINAHL, and the Cochrane Library, from inception to December 30, 2022.

RESULTS

The titles and abstracts of 1,967 unique references were reviewed. After retrieving and reviewing 154 full-text articles, data were extracted from 109 articles, representing 41 CSPBIs covering 21 diseases or conditions. The development of CSPBIs was conceptualized as a 15-step framework, covering four phases: 1) develop initial questionnaire items (when no suitable non-preference-based instrument exists), 2) establish the dimensional structure, 3) reduce items per dimension, 4) value and model health state utilities. Thirty-nine instruments used a type of Rasch model and two instruments used IRT models in phase 3.

CONCLUSION

We present an expanded framework that outlines the development of CSPBIs, both from existing non-preference-based instruments and de novo when no suitable non-preference-based instrument exists, using IRT and Rasch analysis. For items that fit the Rasch model, developers selected one item per dimension and explored item response level reduction. This framework will guide researchers who are developing or assessing CSPBIs.

Development and Valuation of a Preference-Weighted Measure in Age-Related Macular Degeneration From the Vision Impairment in Low Luminance Questionnaire: A MACUSTAR Report

OBJECTIVES

This study generates VILL-UI (Vision Impairment in Low Luminance - Utility Index), a preference-weighted measure (PWM) derived from the VILL-33 measure for use in patients with age-related macular degeneration (AMD) and valued to generate United Kingdom and German preference weights.

METHODS

A PWM consists of a classification system to describe health and utility values for every state described by the classification. The classification was derived using existing data collected as part of the MACUSTAR study, a low-interventional study on AMD, conducted at 20 clinical sites across Europe. Items were selected using psychometric and Rasch analyses, published criteria around PWM suitability, alongside instrument developer views and concept elicitation work that informed VILL-33 development. An online discrete choice experiment (DCE) with duration of the health state was conducted with the United Kingdom and German public. Responses were modeled to generate utility values for all possible health states.

RESULTS

The classification system has 5 items across the 3 domains of VILL-33: reading and accessing information, mobility and safety, and emotional well-being. The DCE samples (United Kingdom: n = 1004, Germany: n = 1008) are broadly representative and demonstrate good understanding of the tasks. The final DCE analyses produce logically consistent and significant coefficients.

CONCLUSIONS

This study enables responses to VILL-33 to be directly used to inform economic evaluation in AMD. The elicitation of preferences from both United Kingdom and Germany enables greater application of VILL-UI for economic evaluation throughout Europe. VILL-UI fills a gap in AMD in which generic preference-weighted measures typically lack sensitivity.

Health state utility estimation of Mycobacterium Avium complex pulmonary disease using a time trade-off approach

AIMS

To obtain appropriate health state utility values for cost-effectiveness analyses of new Mycobacterium avium complex pulmonary disease (MAC-PD) treatments. The impact of MAC-PD severity and symptoms on quality of life (QoL) also were quantified.

METHODS

A questionnaire describing four health states, MAC-positive severe, MAC-positive moderate, MAC-positive mild, and MAC-negative, was developed based on St. George's Respiratory Questionnaire (SGRQ) Symptom and Activity scores from the CONVERT trial. The time trade-off (TTO) method with ping-pong titration procedure was used to estimated health state utilities. Regression analyses assessed impacts of covariates.

RESULTS

Of 319 Japanese adults (49.8% female, mean age 44.8 years), mean (95% CI) health state utility scores (MAC-positive severe, MAC-positive moderate, MAC-positive mild, and MAC-negative) were 0.252 (0.194-0.310), 0.535 (0.488-0.582), 0.816 (0.793-0.839), and 0.881 (0.866-0.896), respectively. MAC-negative state utility scores were significantly higher than MAC-positive severe (mean difference [95% CI], 0.629 [0.574-0.684]), MAC-positive moderate (0.346 [0.304-0.389]), and MAC-positive mild (0.065 [0.048-0.082]) scores (p < 0.001 each). Most participants would trade survival duration to avoid MAC-positive states (97.5% to avoid MAC-positive severe; 88.7% MAC-positive moderate; 61.4% MAC-positive mild). Regression analyses to investigate the impact of background characteristics showed similar utility differences between health states when not adjusted for covariates.

LIMITATIONS

Some participant demographics differed from the general population; however, this did not impact utility differences among health states as regression analyses adjusting for demographics did not affect these differences. Similar investigations are needed among patients with MAC-PD and in other countries.

CONCLUSIONS

This study evaluating the impact of MAC-PD on utilities using the TTO method demonstrates that differences in utilities are dependent on the severity of respiratory symptoms and their impacts on daily activities and QoL. These results could contribute to better quantification of the value of MAC-PD treatments and improve assessments of cost-effectiveness.

Developing the Breast Utility Instrument to Measure Health-Related Quality-of-Life Preferences in Patients with Breast Cancer: Selecting the Item for Each Dimension

Introduction. Generic preference-based instruments inadequately measure breast cancer (BrC) health-related quality-of-life preferences given advances in therapy. Our overall purpose is to develop the Breast Utility Instrument (BUI), a BrC-specific preference-based instrument. This study describes the selection of the BUI items. Methods. A total of 408 patients from diverse BrC health states completed the EORTC QLQ-C30 and BR45 (breast module). For each of 10 dimensions previously assessed with confirmatory factor analysis, we evaluated data fit to the Rasch model based on global model and item fit, including threshold ordering, item residuals, infit and outfit, differential item functioning (age), and unidimensionality. Misfitting items were removed iteratively, and the model fit was reassessed. From items fitting the Rasch model, we selected 1 item per dimension based on high patient- and clinician-rated item importance, breadth of item thresholds, and clinical relevance. Results. Global model fit was good in 7 and borderline in 3 dimensions. Separation index was acceptable in 4 dimensions. Item selection criteria were maximized for the following items: 1) physical functioning (trouble taking a long walk), 2) emotional functioning (worry), 3) social functioning (interfering with social activities), 4) pain (having pain), 5) fatigue (tired), 6) body image (dissatisfied with your body), 7) systemic therapy side effects (hair loss), 8) sexual functioning (interest in sex), 9) breast symptoms (oversensitive breast), and 10) endocrine therapy symptoms (problems with your joints). Conclusions. We propose 10 items for the BUI. Our next steps include assessing the measurement properties prior to eliciting preference weights of the BUI.

Highlights

A previous confirmatory factor analysis established 10 dimensions of the European Organisation for Research and Treatment of Cancer (EORTC) core quality of life questionnaire (QLQ-C30) and its breast module (BR45). In this study, we selected 1 item per dimension based on fit to the Rasch model, patient- and clinician-rated item importance, breadth of item thresholds, and clinical relevance. These items form the core of the future Breast Utility Instrument (BUI). The future BUI will be a novel breast cancer–specific preference-based instrument that potentially will better reflect women’s preferences in clinical decision making and cost utility analyses.

Deriving a Preference-Based Measure for People With Duchenne Muscular Dystrophy From the DMD-QoL

OBJECTIVES

This study generates a preference-based measure for capturing the quality of life of people with Duchenne muscular dystrophy (DMD) from a new measure of quality of life, DMD-QoL.

METHODS

A health state classification system was derived from the DMD-QoL based on psychometric performance of items, factor analysis, and item response theory analysis. Preferences for health states described by the classification system were elicited using an online discrete choice experiment survey with life years as an additional attribute, from members of the UK general population (n = 1043). Discrete choice experiment data was modeled using a conditional fixed-effects logit model and utility estimates were directly anchored on the 1 to 0 full health-dead scale.

RESULTS

The health state classification system has 8 dimensions: mobility, difficulty using hands, difficulty breathing, pain, tiredness, worry, participation, and feeling good about yourself. The standard model had mostly statistically significant coefficients and reflected the instrument's monotonic structure. However, 2 dimensions had inconsistent coefficients (where utility increased as health worsened) and a consistent model was estimated that merged adjacent inconsistent severity levels. The best state defined by the classification system has a value of 1 and the worst state has a value of -0.559.

CONCLUSION

The modeled results enable DMD-QoL-8D utility values to be generated using DMD-QoL or DMD-QoL-8D data to generate QALYs for people with DMD. QALYs can then be used to inform economic models of the cost-effectiveness of interventions in DMD. Future research comparing the psychometric performance of DMD-QoL-8D to existing generic preference-based measures, including EQ-5D-5L, is recommended.

The Functional Assessment of Cancer Therapy Eight Dimension (FACT-8D), a Multi-Attribute Utility Instrument Derived From the Cancer-Specific FACT-General (FACT-G) Quality of Life Questionnaire: Development and Australian Value Set

OBJECTIVES

To develop a cancer-specific multi-attribute utility instrument derived from the Functional Assessment of Cancer Therapy - General (FACT-G) health-related quality of life (HRQL) questionnaire.

METHODS

We derived a descriptive system based on a subset of the 27-item FACT-G. Item selection was informed by psychometric analyses of existing FACT-G data (n = 6912) and by patient input (n = 82). We then conducted an online valuation survey, with participants recruited via an Australian general population online panel. A discrete choice experiment (DCE) was used, with attributes being the HRQL dimensions of the descriptive system and survival duration, and 16 choice-pairs per participant. Utility decrements were estimated with conditional logit and mixed logit modeling.

RESULTS

Eight HRQL dimensions were included in the descriptive system: pain, fatigue, nausea, sleep, work, social support, sadness, and future health worry; each with 5 levels. Of 1737 panel members who accessed the valuation survey, 1644 (95%) completed 1 or more DCE choice-pairs and were included in analyses. Utility decrements were generally monotonic; within each dimension, poorer HRQL levels generally had larger utility decrements. The largest utility decrements were for the highest levels of pain (-0.40) and nausea (-0.28). The worst health state had a utility of -0.54, considerably worse than dead.

CONCLUSIONS

A descriptive system and preference-based scoring approach were developed for the FACT-8D, a new cancer-specific multi-attribute utility instrument derived from the FACT-G. The Australian value set is the first of a series of country-specific value sets planned that can facilitate cost-utility analyses based on items from the FACT-G and related FACIT questionnaires containing FACT-G items.

Development of a preference-based heart disease-specific health state classification system using MacNew heart disease-related quality of life instrument

PURPOSE

The MacNew Heart Disease Health-Related Quality of Life Instrument (MacNew) is a validated, clinically sensitive, 27-item disease-specific questionnaire. This study aimed to develop a new heart disease-specific classification system for the MacNew amenable for use in health state valuation.

METHODS

Patients with heart disease attending outpatient clinics and inpatient wards in Brisbane, Australia, completed MacNew. The development of the new disease-specific classification system included three stages. First, a principal component analysis (PCA) established dimensionality. Second, Rasch analysis was used to select items for each dimension. Third, Rasch analysis was used to explore response-level reduction. In addition, clinician and patient judgement informed item selection.

RESULTS

Participants included 685 patients (acute coronary 6%, stable coronary 41%, chronic heart failure 20%). The PCA identified 4 dimensions (restriction, emotion, perception of others, and symptoms). The restriction dimension was divided into physical and social dimensions. One item was selected from each to be included in the classification system. Three items from the emotional dimension and two symptom items were also selected. The final classification system had seven dimensions with four severity levels in each: physical restriction; excluded from doing things with other people; worn out or low in energy; frustrated, impatient or angry; unsure and lacking in self-confidence; shortness of breath; and chest pain.

CONCLUSION

This study generated a brief heart disease-specific classification system, consisting of seven dimensions with four severity levels in each. The classification system is amenable to valuation to enable the generation of utility value sets to be developed for use in economic evaluation.

Valuation of SF-6Dv2 Health States in China Using Time Trade-off and Discrete-Choice Experiment with a Duration Dimension

OBJECTIVES

Our objective was to generate a value set for the SF-6Dv2 using time trade-off (TTO) and a discrete-choice experiment with a duration dimension (DCE_TTO) in China.

METHODS

A large representative sample of the Chinese general population was recruited from eight provinces/municipalities in China, stratified by age, sex, education level, and proportion of urban/rural residence. Respondents completed eight TTO tasks and ten DCE_TTO tasks during face-to-face interviews. Ordinary least squares (OLS), random-effects, fixed-effects, and Tobit models were used for TTO data, and conditional logit and mixed logit models were used for DCE_TTO. The monotonicity of model coefficients and the consistency of the predicted values according to intraclass correlation coefficient (ICC), mean absolute difference (MAD), and mean squared difference (MSD) were compared between the two approaches.

RESULTS

In total, 3320 respondents (50.3% male; range 18-90 years) were recruited. The random-effects model and the conditional logit model were preferred for the TTO and DCE_TTO, respectively. The TTO values ranged from - 0.277 to 1, with 927 (4.94%) states considered as worse than dead (WTD). The corresponding range for DCE_TTO was - 0.535 to 1, with a higher WTD of 8.50%. DCE_TTO presented minor non-monotonicity with the coefficients in two dimensions. Values from the two approaches were highly consistent (ICC 0.9804, MAD 0.0588, MSD 0.0055), albeit those with DCE_TTO were slightly lower than those with TTO. The value set generated by TTO was preferred given the better monotonicity and the statistical significance of coefficients.

CONCLUSIONS

The Chinese value set for the SF-6Dv2 was established based on the TTO approach, but the DCE_TTO also performed well. Minor issues of non-monotonicity did present for DCE_TTO.

Developing an early childhood oral health impact-specific health-state classification system for a new preference-based instrument, the ECOHIS-4D

OBJECTIVES

Most of the paediatric quality-of-life instruments in oral health research are not preference-based measures, thus cannot be used in economic evaluations. The Early Childhood Oral Health Impact Scale (ECOHIS) is one such instrument which assesses oral health impact on children's quality of life among three- to five-year-olds. With increasing demands for more resource allocation in oral health care, there is a need for an outcome metric which can be used as the outcome in economic evaluations. The aim of this study was to develop a preference-based, health-state classification system from the existing ECOHIS instrument.

METHODS

The 13-item ECOHIS instrument was applied to the carers of 280 preschoolers who participated in a clinical trial in metropolitan Perth, Western Australia. Exploratory factor analysis was conducted to assess the dimensionality of the ECOHIS. Rasch analysis was used to eliminate and select items per determined dimensions and to reduce the number of response options for each item, because six levels were not amenable to valuation. The final classification system was determined through a combination of psychometric and expert advice.

RESULTS

Items loaded on to four dimensions with the factor analysis. Using Rasch analysis, three items were selected from the first dimension (child functional and psychological impact) and one item from the second dimension (child social interaction impact). None were selected from the third dimension (parental distress and family function impact) as it is not an appropriate proxy method of measuring the level of impact on a child. The final classification system has four dimensions with three levels in each, and so it has been named the ECOHIS-4D.

CONCLUSIONS

The ECOHIS-4D classification system is a new preference-based instrument derived from the existing ECOHIS instrument. Future valuation studies will develop a utility value set for the health states defined by this classification system, and this will guide economic evaluations related to oral health interventions.

United States Utility Algorithm for the EORTC QLU-C10D, a Multiattribute Utility Instrument Based on a Cancer-Specific Quality-of-Life Instrument

BACKGROUND

The EORTC QLU-C10D is a multiattribute utility measure derived from the cancer-specific quality-of-life questionnaire, the EORTC QLQ-C30. The QLU-C10D contains 10 dimensions (physical, role, social and emotional functioning, pain, fatigue, sleep, appetite, nausea, bowel problems). The objective of this study was to develop a United States value set for the QLU-C10D.

METHODS

A US online panel was quota recruited to achieve a representative sample for sex, age (≥18 y), race, and ethnicity. Respondents undertook a discrete choice experiment, each completing 16 choice-pairs, randomly assigned from a total of 960 choice-pairs. Each pair included 2 QLU-C10D health states and duration. Data were analyzed using conditional logistic regression, parameterized to fit the quality-adjusted life-year framework. Utility weights were calculated as the ratio of each dimension-level coefficient to the coefficient for life expectancy.

RESULTS

A total of 2480 panel members opted in, 2333 (94%) completed at least 1 choice-pair, and 2273 (92%) completed all choice-pairs. Within dimensions, weights were generally monotonic. Physical functioning, role functioning, and pain were associated with the largest utility weights. Cancer-specific dimensions, such as nausea and bowel problems, were associated with moderate utility decrements, as were general issues such as problems with emotional functioning and social functioning. Sleep problems and fatigue were associated with smaller utility decrements. The value of the worst health state was 0.032, which was slightly greater than 0 (equivalent to being dead).

CONCLUSIONS

This study provides the US-specific value set for the QLU-C10D. These estimated health state scores, based on responses to the EORTC QLQ-C30 questionnaire, can be used to evaluate the cost-utility of oncology treatments.

How do core autism traits and associated symptoms relate to quality of life? Findings from the Longitudinal European Autism Project

Previous studies have reported reduced quality of life in autism. Improving quality of life for autistic people is, therefore, a key priority for clinical research and practice. However, the relative impact of core autism traits (e.g. social-communication difficulties), as compared to associated mental health symptoms (e.g. anxiety, depression) on quality of life remains poorly understood. This is despite at least 20%–50% of autistic individuals experiencing associated anxiety and/or depression symptoms. Hence, we measured subjective quality of life in 573 six to thirty-year-olds (autism spectrum disorder N = 344), using two widely validated questionnaires. Adults self-reported on the World Health Organization Quality of Life–Brief instrument. Parents of children/adolescents completed the Child Health and Illness Profile. We assessed individual variability across both measures and modelled associations between quality of life, core autism traits, anxiety, and depression symptoms. Across both age groups and quality of life measures, autistic individuals scored lower than comparison individuals, on average, particularly for physical health in adults (d = −1.24, 95% confidence interval: [−1.56, −0.93]) and school achievement for children/adolescents (d = −1.06, 95% confidence interval: [−1.29, −0.84]). However, a notable proportion of autistic individuals (36%–71% across quality of life domains) did not have reduced quality of life. Across ages and quality of life measures, severity of associated symptoms was significantly related to reduced quality of life on several domains, after accounting for core autism traits. Most notably, depression symptoms were related to reduced physical/psychological well-being in both adults (β ⩾ −0.34) and children/adolescents (β = −0.29, 95% confidence interval: [−0.36, −0.14]). For children/adolescents, anxiety symptoms (β ⩾ −0.28) and core social-communication difficulties (β ⩾ −0.22) were also related to subjective quality of life outcomes. Overall, findings indicate that not all autistic individuals experience reduced subjective quality of life. Variability in quality of life is significantly influenced by associated symptoms, across developmental stage. This may provide a tractable target for mental health services to improve quality of life for autistic individuals over the lifespan.

U.K. utility weights for the EORTC QLU-C10D

The EORTC QLU-C10D is a new multi-attribute utility instrument derived from the widely used cancer-specific quality of life questionnaire, EORTC QLQ-C30. It contains 10 dimensions (physical functioning, role functioning, social functioning, emotional functioning, pain, fatigue, sleep, appetite, nausea, bowel problems), each with four levels. The aim of this study was to provide U.K. general population utility weights for the QLU-C10D. A U.K. online panel was quota-sampled to align the sample to the general population proportions of sex and age (≥18 years). The online valuation survey included a discrete choice experiment (DCE). Each participant was asked to complete 16 choice-pairs, each comprising two QLU-C10D health states plus duration. DCE data were analysed using conditional logistic regression to generate utility weights. Data from 2,187 respondents who completed at least one choice set were included in the DCE analysis. The final U.K. QLU-C10D utility weights comprised decrements for each level of each health dimension. For nine of the 10 dimensions (all except appetite), the expected monotonic pattern was observed across levels: Utility decreased as severity increased. For the final model, consistent monotonicity was achieved by merging inconsistent adjacent levels for appetite. The largest utility decrements were associated with physical functioning and pain. The worst possible health state (the worst level of each dimension) is -0.083, which is considered slightly worse than being dead. The U.K.-specific utility weights will enable cost-utility analysis (CUA) for the economic evaluation of new oncology therapies and technologies in the United Kingdom, where CUA is commonly used to inform resource allocation.

The EORTC QLU-C10D: The Canadian Valuation Study and Algorithm to Derive Cancer-Specific Utilities From the EORTC QLQ-C30

Objective. The EORTC QLQ-C30 is widely used for assessing quality of life in cancer. However, QLQ-C30 responses cannot be incorporated in cost-utility analysis because they are not based on general population's preferences, or utilities. To overcome this limitation, the QLU-C10D, a cancer-specific utility algorithm, was derived from the QLQ-C30. The aim of this study was to obtain Canadian population utility weights for the QLU-C10D. Methods. Respondents from a Canadian research panel expressed their preferences for 16 choice sets in an online discrete choice experiment. Each choice set consisted of two health states described by the 10 QLU-C10D domains plus an attribute representing duration of survival. Using a conditional logit model, responses were converted into utility decrements by evaluating the marginal rate of substitution between each QLU-C10D domain level with respect to duration. Results. A total of 3,363 individuals were recruited. A total of 2,345 completed at least one choice set and 2,271 completed all choice sets. The largest utility decrements were associated with the worse levels of Physical Functioning (-0.24), Pain (-0.18), Role Functioning (-0.15), Emotional Functioning (-0.12), and Nausea (-0.12). The remaining domains and levels had decrements of -0.05 to -0.09. The utility of the worst possible health state was -0.15. Conclusion. Respondents from the general population were most concerned with generic health domains, but Nausea and Bowel Problems also had an impact on the individual's utility. It is unclear as to whether cancer-specific domains will affect cost-utility analysis when evaluating cancer treatments; this will be tested in the next phase of the study.

EXPAND, a dose-finding study of ruxolitinib in patients with myelofibrosis and low platelet counts: 48-week follow-up analysis

EEXPAND (phase Ib, dose-finding study) evaluated the starting dose of ruxolitinib in patients with myelofibrosis with baseline platelet counts of 50-99×10⁹/L. The study consisted of dose-escalation and safety-expansion phases. Based on the baseline platelet counts, patients were assigned to stratum 1 (75-99×10⁹/L) or stratum 2 (50-74×10⁹/L), with the primary objective of determining the maximum safe starting dose (MSSD); key secondary objectives included safety and efficacy. At week 48 data cutoff (stratum 1, n=44; stratum 2, n=25), 24.6% (17 out of 69) of patients were still receiving treatment. The MSSD was established as ruxolitinib 10 mg twice daily in both strata. Thrombocytopenia [grade 4 (stratum 1, n=1; stratum 2, n=2)] was the only reported dose-limiting toxicity (study drug related) at 10 mg twice daily. In the MSSD cohort (stratum 1, n=20; stratum 2, n=18), adverse events (regardless of study drug relationship) led to treatment discontinuation in 15.0% and 33.3% of patients in stratum 1 and stratum 2, respectively, and dose adjustment/interruption in 45.0% and 66.7% of patients in stratum 1 and stratum 2, respectively. Three cases of on-treatment deaths were reported at the MSSD. Spleen response was achieved at week 48 in 33.3% and 30.0% of patients in stratum 1 and stratum 2, respectively. Improvements in the Total Symptom Score were also observed. In this study, ruxolitinib demonstrated acceptable tolerability in both the strata at the MSSD of 10 mg twice daily. (Registered at: clinicaltrials.gov identifier: 01317875).

Estimating a Dutch Value Set for the Pediatric Preference-Based CHU9D Using a Discrete Choice Experiment with Duration

OBJECTIVE

This article presents the development of the Dutch value set for the Child Health Utility 9D, a pediatric preference-based measure of quality of life that can be used to generate quality-adjusted life-years.

METHODS

A large online survey was conducted using a discrete choice experiment including a duration attribute with adult members of the Netherlands general population (N = 1276) who were representative in terms of age, gender, marital status, employment, education, and region. Respondents were asked which of two health states they prefer, where each health state was described using the nine dimensions of the Child Health Utility 9D (worried, sad, pain, tired, annoyed, school work/homework, sleep, daily routine, able to join in activities) and duration. The data were modeled using conditional logit with robust standard errors to produce utility values for every health state described by the Child Health Utility 9D.

RESULTS

The majority of the dimension level coefficients were monotonic, leading to a decrease in utility as severity increases. There was, however, evidence of some logical inconsistencies, particularly for the school work/homework dimension. The value set produced was based on the ordered model and ranges from -0.568 for the worst state to 1 for the best state.

CONCLUSION

The valuation of the Child Health Utility 9D using online discrete choice experiment with duration with adult members of the Dutch general population was feasible and produced a valid model for use in cost utility analysis. Normative questions are raised around the valuation of pediatric preference-based measures, including the appropriate perspective for imagining hypothetical pediatric health states.

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

BACKGROUND

The EORTC QLU-C10D is a new multi-attribute utility instrument derived from the widely used cancer-specific quality-of-life (QOL) questionnaire, EORTC QLQ-C30. The QLU-C10D contains ten dimensions (Physical, Role, Social and Emotional Functioning; Pain, Fatigue, Sleep, Appetite, Nausea, Bowel Problems), each with four levels. To be used in cost-utility analysis, country-specific valuation sets are required.

OBJECTIVE

The aim of this study was to provide Australian utility weights for the QLU-C10D.

METHODS

An Australian online panel was quota-sampled to ensure population representativeness by sex and age (≥ 18 years). Participants completed a discrete choice experiment (DCE) consisting of 16 choice-pairs. Each pair comprised two QLU-C10D health states plus life expectancy. Data were analysed using conditional logistic regression, parameterised to fit the quality-adjusted life-year framework. Utility weights were calculated as the ratio of each QOL dimension-level coefficient to the coefficient on life expectancy.

RESULTS

A total of 1979 panel members opted in, 1904 (96%) completed at least one choice-pair, and 1846 (93%) completed all 16 choice-pairs. Dimension weights were generally monotonic: poorer levels within each dimension were generally associated with greater utility decrements. The dimensions that impacted most on choice were, in order, Physical Functioning, Pain, Role Functioning and Emotional Functioning. Oncology-relevant dimensions with moderate impact were Nausea and Bowel Problems. Fatigue, Trouble Sleeping and Appetite had relatively small impact. The value of the worst health state was -0.096, somewhat worse than death.

CONCLUSIONS

This study provides the first country-specific value set for the QLU-C10D, which can facilitate cost-utility analyses when applied to data collected with the EORTC QLQ-C30, prospectively and retrospectively.

The Role of Condition-Specific Preference-Based Measures in Health Technology Assessment

A condition-specific preference-based measure (CSPBM) is a measure of health-related quality of life (HRQOL) that is specific to a certain condition or disease and that can be used to obtain the quality adjustment weight of the quality-adjusted life-year (QALY) for use in economic models. This article provides an overview of the role and the development of CSPBMs, and presents a description of existing CSPBMs in the literature. The article also provides an overview of the psychometric properties of CSPBMs in comparison with generic preference-based measures (generic PBMs), and considers the advantages and disadvantages of CSPBMs in comparison with generic PBMs. CSPBMs typically include dimensions that are important for that condition but may not be important across all patient groups. There are a large number of CSPBMs across a wide range of conditions, and these vary from covering a wide range of dimensions to more symptomatic or uni-dimensional measures. Psychometric evidence is limited but suggests that CSPBMs offer an advantage in more accurate measurement of milder health states. The mean change and standard deviation can differ for CSPBMs and generic PBMs, and this may impact on incremental cost-effectiveness ratios. CSPBMs have a useful role in HTA where a generic PBM is not appropriate, sensitive or responsive. However, due to issues of comparability across different patient groups and interventions, their usage in health technology assessment is often limited to conditions where it is inappropriate to use a generic PBM or sensitivity analyses.

Cost-effectiveness of Ruxolitinib vs Best Available Therapy in the Treatment of Myelofibrosis in Spain

Introduction: Primary myelofibrosis (MF) is a rare hematologic disease belonging to the group of Philadelphia-negative chronic myeloproliferative neoplasms. Identification of the Janus Kinase (JAK) gene mutations inaugurated a new era in the targeted therapy of myeloproliferative diseases. Ruxolitinib is the first JAK1/JAK2 inhibitor specifically approved for the treatment of disease-related splenomegaly or symptoms in adult patients with primary myelofibrosis. The objective of this study was to assess the cost-effectiveness of ruxolitinib vs best available therapy (BAT) in MF patients in Spain. Methods: A decision-tree and Markov model were adapted to the Spanish setting to assess the cost-effectiveness of ruxolitinib vs. BAT on a lifetime horizon (≤15 years) from the societal perspective, while healthcare system perspective was included in the one-way sensitivity analysis. The population was assumed to be similar to that of the COMFORT-II clinical trial (CT), which was also the source of treatment efficacy data. BAT composition was derived from the same CT and validated with Spanish experts. Utilities were derived from the COMFORT-I CT. Costs included treatment, management, hospitalizations, emergency and outpatient visits, as well as adverse events and end-of-life costs. Additionally, costs associated to productivity loss were taken into account. Resource use was validated with experts and costs were extracted from Spanish sources. A probabilistic sensitivity analysis was also performed to evaluate the consistency of the results under the uncertainty or variability of the input data. Results: Patients on ruxolitinib accumulated 6.1 life years gained (LYGs), resulting in 73% extra life-years compared to patients treated with BAT (3.5LYs gained). Ruxolitinib provided 4.4 quality-adjusted life years (QALYs), with a 99% improvement compared to BAT (2.2 QALYs). This analysis gave an incremental cost of €47 199 per LYG and an incremental cost of €55 616 per QALY gained from the societal perspective. Conclusions: Ruxolitinib would be cost-effective in Spain according to the end-of-life criteria defined by the NICE and commonly referred for Spain (cost-effectiveness threshold of €61 500/QALY), in line with results published for other European countries.

Developing preference-based measures for diabetes: DHP-3D and DHP-5D

AIMS

The aim of this study was to develop two diabetes-specific preference-based measures [the Diabetes Health Profile-3 Dimension (DHP-3D) and the Diabetes Health Profile-5 Dimension (DHP-5D)] for use in the calculation of Quality Adjusted Life Years, a key outcome in economic evaluation. These measures were based on the non-preference-based instrument the Diabetes Health Profile.

METHODS

For DHP-3D, psychometric and Rasch analyses were used to develop a health state classification system based on the Diabetes Health Profile-18 (DHP-18). The DHP-5D added two dimensions to the DHP-3D to extend the range of impacts measured. Each classification system was valued by 150 general public respondents in the United Kingdom using Time Trade Off (TTO). Multivariate regression was used to estimate utility value sets. The matched dimensions across each measure were compared using z-score tests.

RESULTS

The DHP-3D included three dimensions defined as mood, eating and social limitations, and the DHP-5D added dimensions defined as hypoglycaemic attacks and vitality. For both, the random effects generalized least squares regression model produced consistent value sets, with the DHP-3D and DHP-5D ranging from 0.983 (best state) to 0.717 (worst state), and 0.979 to 0.618 respectively. The addition of the two extra dimensions leads to significant differences for the more severe levels of each matched dimension.

CONCLUSIONS

We have developed two diabetes-specific preference-based measures that, subject to psychometric assessment, can be used to provide condition-specific utility values to complement generic utilities from more widely validated measures such as the EuroQol-5 Dimension.

Cost-effectiveness of kinase inhibitors for hematologic malignancies: a systematic and critical review

INTRODUCTION

Several genetic disruptions lead to constitutive activation of those kinases leukemic cells depend on for survival and proliferation. Kinase inhibitors (KI) are major therapeutic innovations for chronic myeloid leukemia (CML), chronic lymphoid leukemia (CLL) and myelofibrosis (MF) providing a relevant improvement of quality-adjusted survival in patients with high-risk or refractory disease. CML patients are being treated with first-generation KI imatinib since many years, achieving expected survivals longer than 10 years. Second- and third generations KIs, such as nilotinib, dasatinib, ponatinib and bosutinib, recently expanded the therapeutic yield for CML and treatment discontinuation in patients with persistent deep molecular response is being pursued. Areas covered: This review summarizes available evidence on economic analyses of KI treatments for CML, CLL and MF aimed at identifying the key determinants of KI cost-effectiveness. Expert commentary: On converse, specific KIs for CLL and MF patients have been marketed only in the last few years. Ibrutinib and idelalisib allowed to improve the outcomes of relapsed/refractory CLL and of patients with poor genetic features, while the first-in-class JAK2 inhibitor ruxolitinib allowed to improve symptoms of advanced MF patients and to prolong survival in responders. In the current situation of healthcare budget restrictions worldwide, the value for cost of the above KIs has been questioned.

A Review of Ruxolitinib for the Treatment of Myelofibrosis: A Critique of the Evidence

As part of the National Institute for Health and Care Excellence's (NICE) Single Technology Appraisal (STA) process, ruxolitinib was assessed to determine the clinical and cost effectiveness of its use in the treatment of disease-related splenomegaly or symptoms in adults with myelofibrosis. Ruxolitinib had previously been assessed as part of the STA process and was not recommended in NICE guidance issued in June 2013 (TA289). A review of TA289 was commissioned following the availability of new longer-term survival data; a price discount patient access scheme (PAS) was also introduced. The Centre for Reviews and Dissemination (CRD) and Centre for Health Economics (CHE) Technology Appraisal Group at the University of York was commissioned to act as the independent Evidence Review Group (ERG). This article provides a summary of the manufacturer or sponsor of the technology's (referred to as the company) submission, the ERG review and the resulting NICE guidance issued in March 2016. The main clinical effectiveness data were derived from two good-quality multicentre randomised controlled trials (RCTs): COMFORT-II compared ruxolitinib with best available therapy (BAT) and COMFORT-I compared ruxolitinib with placebo. Both RCTs demonstrated a statistically significant reduction in splenomegaly and its associated symptoms in intermediate-2 and high-risk myelofibrosis patients. Overall survival was statistically significantly improved with ruxolitinib compared with BAT at 3.5 years of follow-up in the COMFORT-II trial (hazard ratio 0.58, 95 % CI 0.36-0.93). Grade 3-4 adverse events were more frequent in the ruxolitinib group than in the BAT group; 42 % compared with 25 %. Evidence relating to patients with lower-risk disease or low platelet counts (50-100 × 10⁹/L) was less robust. The company's economic model was well-presented and had an appropriate model structure. The base-case incremental cost-effectiveness ratio (ICER) was estimated to be around £45,000 per quality-adjusted life-year (QALY) gained (including the PAS discount). Extensive sensitivity and scenario analyses were presented, demonstrating that the estimated ICER was robust to a range of input values and assumptions made in the model. Alternative scenarios presented by the ERG showed only modest increases in the estimated ICER, primarily as a result of including an element of drug wastage within the model. Alternative scenarios resulted in estimated ICERs ranging from around £45,000 to £49,000 per QALY gained (including the PAS discount). At the first appraisal meeting, the NICE Appraisal Committee concluded that ruxolitinib was clinically effective and was a cost effective use of National Health Service (NHS) resources for patients with high-risk myelofibrosis who meet NICE's end-of-life criteria. Following the consultation, the company offered a revised PAS, resulting in a revised base-case ICER of £31,229 per QALY gained. The company also presented new evidence on the cost effectiveness of ruxolitinib in intermediate-2 and high-risk subgroups and a revised version of the model. The NICE Appraisal Committee considered the new evidence and recommended ruxolitinib for the treatment of patients with intermediate-2-risk disease as well as patients with high-risk disease, based on International Prognostic Scoring System (IPSS) prognostic factors.