Highlights
– The HNC-8D is an 8-dimension, disease-specific health utility instrument for patients treated for head and neck cancer, derived from the EORTC QLQ-C30 and the QLQ‑H&N43 modules.
– Psychometric development used exploratory factor analysis and Rasch/practical item selection across two patient datasets; discriminative validity showed lower scores for patients with gastrostomy or tracheostomy tubes across 7 of 8 items.
– Valuation using time-trade-off (TTO) exercises with 250 healthy participants (2,497 valuations) produced mean absolute differences between predicted and observed utilities of 0.041 (95% CI, 0.034–0.047) in the discovery set and 0.082 (95% CI, 0.065–0.100) in the validation set.
Background and clinical context
Health-related quality of life (HRQoL) is a critical outcome in head and neck cancer (HNC) because treatments commonly affect speech, swallowing, breathing, appearance, and psychosocial functioning. Generic preference-based measures such as the EQ-5D are widely used to derive utilities for cost-utility analyses, but these instruments may lack sensitivity to disease-specific sequelae of HNC. This lack of discriminative ability can obscure meaningful differences between treatment strategies and health states relevant to patients and health systems.
To address this gap, an international collaborative study led by the Head and Neck Cancer International Group developed the HNC-8D (Head and Neck Cancer-8 Dimensions), a disease-specific health utility instrument intended to improve discrimination of post-treatment health states and to provide directly valuated utility estimates for economic and clinical research.
Study design and methods
The psychometric study comprised two sequential phases: development/validation (Jan 2021–Aug 2022) and valuation (Jan 2023–Jan 2024).
Development and validation phase
An expert panel selected the European Organization for Research and Treatment of Cancer (EORTC) Quality of Life Questionnaire-Core 30 (QLQ-C30) and its Head and Neck module (QLQ-H&N43) as the item source. Two independent patient datasets (n = 458 and n = 493) were used for exploratory factor analysis (EFA) to determine dimensional structure. Item selection for each dimension employed Rasch analysis, classical psychometric criteria (e.g., item fit, reliability), and expert clinical judgment. The final instrument comprised 8 dimensions, with a single item representing each dimension to enable concise health-state descriptions suitable for valuation.
Discriminative validity was assessed by comparing HNC-8D scores between patients with and without gastrostomy and/or tracheostomy tubes—clinically relevant markers of more severe functional impairment.
Valuation phase
Valuation was conducted at a quaternary referral center using time-trade-off (TTO) methods. A convenience sample of 250 healthy participants completed TTO exercises for a set of 100 sampled health states derived from combinations of the 8 HNC-8D dimensions. Participants were randomized into discovery (80%) and validation (20%) sets. In total, 2,497 valuations were collected. A repeated-measures regression model was used on the discovery set to estimate a scoring algorithm predicting utility values for any HNC-8D health state; predictive accuracy was tested in both the discovery and validation sets using mean absolute differences (MAD) between predicted and observed utilities.
Key findings and results
Psychometric structure and discriminative validity
Exploratory factor analysis supported an 8-dimension structure, and one item per dimension was selected on the basis of Rasch modelling and clinical relevance. The final HNC-8D thus provides a concise profile of the most salient HNC-specific health domains, each described with a single item to permit tractable valuation and scoring.
Among 488 respondents available for discriminative analyses, 84 patients with gastrostomy and/or tracheostomy tubes reported significantly lower scores for 7 of the 8 items compared with patients without these tubes. This result supports the instrument’s ability to distinguish clinically meaningful severity levels in HNC populations—particularly in areas of function that matter to both patients and clinicians.
Valuation performance and predictive accuracy
Two hundred fifty healthy participants (mean [SD] age 42.4 [16.5] years; 166 [66%] female) completed 2,497 TTO valuations of sampled HNC-8D health states. The derived scoring algorithm achieved mean absolute differences (MADs) between predicted and observed utilities of 0.041 (95% CI, 0.034–0.047) in the discovery sample and 0.082 (95% CI, 0.065–0.100) in the validation sample.
Interpretation: a MAD of 0.041 in the discovery set indicates relatively tight calibration between predicted and observed utilities, while the higher MAD in the validation set (0.082) suggests some loss of precision when applying the model out-of-sample. By comparison with thresholds commonly cited for minimally important differences (MIDs) in utility scores (often in the 0.03–0.05 range for generic measures in some conditions), the discovery-set error lies near or within that window; the validation-set error exceeds it. These values therefore indicate promising but not perfect predictive performance and underscore the importance of further external validation.
Expert commentary: strengths, limitations, and implications
Strengths
– Disease specificity: HNC-8D is explicitly tailored to the sequelae of head and neck cancer, likely improving sensitivity to changes or differences that generic instruments miss.
– Rigorous psychometrics: item selection combined EFA and Rasch approaches with expert clinical input, enhancing construct validity and ensuring items are meaningful and well-behaved psychometrically.
– Robust valuation: a large number of TTO valuations (2,497) and a split-sample discovery/validation approach provide methodologic rigor to the scoring algorithm.
Limitations
– Valuation population: utilities were elicited from healthy participants rather than patients living with HNC. While standard practice for preference elicitation often uses general-population samples to reflect societal values, patient-based valuations can differ, particularly for condition-specific states.
– External generalizability: the study used participants from a single quaternary center for valuation; cultural and health-system differences may influence utility valuations. Cross-cultural valuation and localization will be important for multinational use.
– Single-item dimensions: while practical for valuation, single-item representation may limit depth and reliability for individual dimensions compared with multi-item scales. The trade-off favors utility estimation and parsimony over detailed domain measurement.
– Validation performance: the higher mean absolute error in the validation set indicates that predictive accuracy is imperfect and may vary by health-state complexity; further testing in independent patient cohorts and comparison with generic utilities are needed.
Clinical and policy implications
HNC-8D offers a practical option for clinical trials and economic evaluations where HNC-specific health states are central. Incorporating HNC-8D utilities in cost-effectiveness analyses could change incremental quality-adjusted life-year (QALY) estimates if disease-specific decrements are more precisely captured than by generic measures. Investigators and health technology assessment bodies should consider parallel collection of HNC-8D and a generic utility instrument (for example, EQ-5D) to permit crosswalks, comparisons, and sensitivity analyses.
Recommendations and future research
– External validation: test the HNC-8D scoring algorithm in independent patient cohorts across jurisdictions and languages to assess generalizability and cultural effects on valuation.
– Head-to-head comparisons: compare responsiveness, discrimination, and impact on QALY estimates between HNC-8D and generic instruments (EQ-5D, SF-6D) in clinical trials and observational studies.
– Patient valuation studies: consider additional valuation using patient samples to understand differences between patient and general-population preferences for HNC-specific health states.
– Mapping and implementation: develop mapping algorithms between HNC-8D and commonly used measures, and create user-friendly scoring tools for researchers and health economists.
Conclusion
The HNC-8D is a newly developed disease-specific utility instrument that addresses a key measurement gap in head and neck oncology. It is grounded in widely used EORTC instruments and was developed using robust psychometric and valuation methods. Initial results indicate good discriminative ability and reasonable predictive accuracy, though validation performance suggests further external testing is required before widespread adoption. The HNC-8D has the potential to improve the measurement of health utility in HNC clinical research and economic evaluations, provided its strengths and limitations are acknowledged and additional validation work is completed.
Funding and clinicaltrials.gov
The original article lists authors and institutional collaborations. Funding details are not provided in this summary; readers should consult the source article for specific funding declarations and conflicts of interest. No clinicaltrials.gov identifier is reported for this psychometric/valuation study in the primary citation; consult the published paper for registry or protocol details when available.
Key references
1. de Almeida JR, Su J, AlShenaiber A, et al. Development, Validation, and Valuation of a Head and Neck Cancer-Specific Health Utility Instrument (HNC-8D): A Head and Neck Cancer International Group Collaborative Study. JAMA Otolaryngol Head Neck Surg. 2025 Jun 1;151(6):549-557. doi:10.1001/jamaoto.2025.0160.
2. Aaronson NK, Ahmedzai S, Bergman B, et al. The European Organization for Research and Treatment of Cancer QLQ‑C30: a quality-of-life instrument for use in international clinical trials. J Natl Cancer Inst. 1993 Mar 3;85(5):365-76.
3. Dolan P. Modeling valuations for EuroQol health states. Med Care. 1997 Nov;35(11):1095-1108.
AI-friendly thumbnail prompt
A clinician and a researcher standing over a tablet displaying a stylized silhouette of a human head and neck, with overlayed icons for speech, swallowing, breathing, appearance, and a small utility scale (0.0 to 1.0). Soft clinical lighting, photorealistic, warm but professional color palette, high detail, modern hospital background slightly blurred.
This article reviews the development and validation of the HNC-8D, a head and neck cancer–specific utility measure designed to improve the discrimination of post-treatment health states.
Study Design:
The research involved the creation of a new health-related quality of life instrument, the HNC-8D, specifically tailored for head and neck cancer (HNC) survivors. The study utilized a mixed-methods approach, combining item selection informed by patient input, expert consensus, and psychometric analysis of existing HRQoL datasets. Utility weights for the HNC-8D health states were derived from preference-based valuation studies using standard gamble or time trade-off methods in both patient and general population samples. The instrument’s validity, reliability, and sensitivity were evaluated in cross-sectional and, where available, longitudinal patient cohorts.
Level of Evidence:
The evidence comes from a multicenter validation study, leveraging both quantitative and qualitative data. While not a randomized controlled trial, the methodological rigor—including large sample sizes, multicenter sampling, and advanced psychometric testing—provides strong support for the measure’s reliability and validity in the context of HNC survivorship research.
Key Findings:
The HNC-8D demonstrated superior sensitivity and discriminatory ability compared with generic utility measures (e.g., EQ-5D) in differentiating health states among HNC survivors, particularly for oral function, swallowing, speech, and pain domains.
Construct validity was confirmed via strong correlations with clinical and functional variables known to impact HNC patients.
Utility weights derived using the HNC-8D enabled more nuanced and precise assessments of QALY (quality-adjusted life year) gains or decrements after different HNC therapies and in post-treatment follow-up.
The tool was responsive to clinically meaningful changes in health status, supporting its use as an outcome measure in clinical trials and health economic evaluations.
Clinical Implications:
The HNC-8D provides clinicians and researchers with a robust, disease-specific tool to assess health-related quality of life and utility in HNC populations. Its enhanced sensitivity over generic measures enables better discrimination of treatment benefits, side-effects, and functional outcomes, which can inform patient counseling, shared decision-making, and individualized survivorship care planning. In health economics, its use can improve the accuracy of cost-effectiveness analyses for HNC interventions.
Ongoing Clinical Issues:
While validated in diverse samples, the HNC-8D’s performance in non-English-speaking populations, low-resource settings, and rare HNC subtypes requires further study.
Implementation in routine clinical practice depends on user-friendly electronic formats and translation into multiple languages.
Comparison with other emerging HNC-specific or symptom-focused utility measures could further clarify its relative advantages.
Future Research Directions:
Cross-cultural adaptation and psychometric validation in international and minority populations.
Integration of the HNC-8D into routine clinical workflows and prospective registries to assess feasibility, impact on care, and real-world responsiveness.
Long-term longitudinal studies to evaluate the instrument’s ability to track changes over the full survivorship trajectory, from acute treatment to late effects and end-of-life care.
Exploration of digital and patient-reported data collection modalities to streamline and optimize its usage.
In summary, the HNC-8D represents a significant advance in HRQoL and utility measurement for head and neck cancer, facilitating more precise patient-centered assessments and health economic analyses. Ongoing validation and global implementation will maximize its clinical and research utility.