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Learn more: PMC Disclaimer | PMC Copyright Notice JAMA Otolaryngol Head Neck Surg . 2025 Apr 10;151(6):549–557. doi: 10.1001/jamaoto.2025.0160 Search in PMC Search in PubMed View in NLM Catalog Add to search 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 John R de Almeida John R de Almeida , MD, MSc 1 Department of Otolaryngology−Head and Neck Surgery, University Health Network, Princess Margaret Cancer Centre, University of Toronto, Toronto, Ontario, Canada 2 Institute of Health Policy, Management, and Evaluation, University of Toronto, Toronto, Ontario, Canada Find articles by John R de Almeida 1, 2, ✉ , Jie Su Jie Su , PhD 3 Department of Biostatistics, Princess Margaret Cancer Center, Toronto, Ontario, Canada Find articles by Jie Su 3 , Abdullah AlShenaiber Abdullah AlShenaiber , BHSc 4 Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada Find articles by Abdullah AlShenaiber 4 , Hesameddin Noroozi Hesameddin Noroozi , MD, MPH 1 Department of Otolaryngology−Head and Neck Surgery, University Health Network, Princess Margaret Cancer Centre, University of Toronto, Toronto, Ontario, Canada Find articles by Hesameddin Noroozi 1 , Matthias Buttner Matthias Buttner , MD 5 Institute of Medical Biostatistics, Epidemiology, and Informatics, University Medical Center, Mainz, Germany Find articles by Matthias Buttner 5 , David P Goldstein David P Goldstein , MD, MSc 1 Department of Otolaryngology−Head and Neck Surgery, University Health Network, Princess Margaret Cancer Centre, University of Toronto, Toronto, Ontario, Canada Find articles by David P Goldstein 1 , Aaron Hansen Aaron Hansen , BSc, MBBS 6 Department of Medical Oncology, Princess Margaret Cancer Center, Toronto, Ontario, Canada Find articles by Aaron Hansen 6 , Luiz P Kowalski Luiz P Kowalski , MD, PhD 7 Department of Head and Neck Surgery, A.C. Camargo Cancer Center, University of São Paolo Medical School, São Paolo, Brazil Find articles by Luiz P Kowalski 7 , Lisa Licitra Lisa Licitra , MD 8 Department of Medical Oncology, Fondazione Istituto di Ricovero e Cura a Carattere Scientifico, Istituto Nazionale dei Tumori, Milan, Italy Find articles by Lisa Licitra 8 , Hisham Mehanna Hisham Mehanna , MD 9 Institute of Head and Neck Studies and Education, Department of Cancer and Genomic Sciences, College of Medicine and Health, University of Birmingham, Birmingham, United Kingdom Find articles by Hisham Mehanna 9 , Christopher W Noel Christopher W Noel , MD, PhD 10 Department of Otolaryngology−Head and Neck Surgery, The James Comprehensive Cancer Center, The Ohio State University, Columbus Find articles by Christopher W Noel 10 , Ambica Parmar Ambica Parmar , MD 11 Department of Medical Oncology, Odette Cancer Center, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada Find articles by Ambica Parmar 11 , Sandro Porceddu Sandro Porceddu , MBBS, MD 12 Department of Radiation Oncology, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia Find articles by Sandro Porceddu 12 , Jolie Ringash Jolie Ringash , BSc, MD, MSc 13 Department of Radiation Oncology, Princess Margaret Cancer Centre, University Health Network, University of Toronto, Toronto, Ontario, Canada Find articles by Jolie Ringash 13 , Simon Rogers Simon Rogers , MD 14 Department of Oral and Maxillofacial Surgery, Wirral University Teaching Hospital, Wirral, United Kingdom Find articles by Simon Rogers 14 , Marcos Antonio Dos Santos Marcos Antonio Dos Santos , MD, PhD 15 Department of Radiation Oncology, Grupo Centro de Orientação e Formação Integral para Adolescentes e Responsáveis, Goiania, Goias, Brazil Find articles by Marcos Antonio Dos Santos 15 , Christian Simon Christian Simon , MD 16 Service d’Oto-rhino-laryngologie et chirurgie cervico-faciale, Centre Hospitalier Universitaire Vaudois, Université de Lausanne, Lausanne, Switzerland Find articles by Christian Simon 16 , Minh-Tam Truong Minh-Tam Truong , MD, MBA 17 Department of Radiation Oncology, Boston Medical Center, Boston University Chobanian & Avedisian School of Medicine, Boston, Massachusetts Find articles by Minh-Tam Truong 17 , Wei Xu Wei Xu , PhD 3 Department of Biostatistics, Princess Margaret Cancer Center, Toronto, Ontario, Canada Find articles by Wei Xu 3 Author information Article notes Copyright and License information 1 Department of Otolaryngology−Head and Neck Surgery, University Health Network, Princess Margaret Cancer Centre, University of Toronto, Toronto, Ontario, Canada 2 Institute of Health Policy, Management, and Evaluation, University of Toronto, Toronto, Ontario, Canada 3 Department of Biostatistics, Princess Margaret Cancer Center, Toronto, Ontario, Canada 4 Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada 5 Institute of Medical Biostatistics, Epidemiology, and Informatics, University Medical Center, Mainz, Germany 6 Department of Medical Oncology, Princess Margaret Cancer Center, Toronto, Ontario, Canada 7 Department of Head and Neck Surgery, A.C. Camargo Cancer Center, University of São Paolo Medical School, São Paolo, Brazil 8 Department of Medical Oncology, Fondazione Istituto di Ricovero e Cura a Carattere Scientifico, Istituto Nazionale dei Tumori, Milan, Italy 9 Institute of Head and Neck Studies and Education, Department of Cancer and Genomic Sciences, College of Medicine and Health, University of Birmingham, Birmingham, United Kingdom 10 Department of Otolaryngology−Head and Neck Surgery, The James Comprehensive Cancer Center, The Ohio State University, Columbus 11 Department of Medical Oncology, Odette Cancer Center, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada 12 Department of Radiation Oncology, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia 13 Department of Radiation Oncology, Princess Margaret Cancer Centre, University Health Network, University of Toronto, Toronto, Ontario, Canada 14 Department of Oral and Maxillofacial Surgery, Wirral University Teaching Hospital, Wirral, United Kingdom 15 Department of Radiation Oncology, Grupo Centro de Orientação e Formação Integral para Adolescentes e Responsáveis, Goiania, Goias, Brazil 16 Service d’Oto-rhino-laryngologie et chirurgie cervico-faciale, Centre Hospitalier Universitaire Vaudois, Université de Lausanne, Lausanne, Switzerland 17 Department of Radiation Oncology, Boston Medical Center, Boston University Chobanian & Avedisian School of Medicine, Boston, Massachusetts Accepted for Publication: January 28, 2025. Published Online: April 10, 2025. doi: 10.1001/jamaoto.2025.0160 ✉ Corresponding Author: John R. de Almeida, MD, MSc, Toronto General Hospital, University Health Network, 200 Elizabeth St, 8N-883, Toronto, ON M5G 2C4, Canada ( [email protected] ). Author Contributions: Dr de Almeida had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Concept and design: de Almeida, Noroozi, Buttner, Mehanna, Noel, Dos Santos, Truong, Xu, Licitra. Acquisition, analysis, or interpretation of data: de Almeida, Su, AlShenaiber, Goldstein, Hansen, Mehanna, Noel, Parmar, Porceddu, Ringash, Rogers, Dos Santos, Simon, Xu, Kowalski. Drafting of the manuscript : de Almeida, AlShenaiber, Noroozi, Mehanna, Dos Santos. Critical review of the manuscript for important intellectual content : Su, AlShenaiber, Buttner, Goldstein, Hansen, Mehanna, Noel, Parmar, Porceddu, Ringash, Rogers, Dos Santos, Simon, Truong, Xu, Licitra, Kowalski. Statistical analysis: de Almeida, Su, Noel, Xu, Kowalski. Administrative, technical, or material support: AlShenaiber, Noroozi, Mehanna, Noel, Truong. Supervision: de Almeida, Hansen, Dos Santos, Simon, Xu, Licitra. Other−clinical context and meaning: Rogers. Othe−discussions in Delphi process : Buttner. Conflict of Interest Disclosures: Dr de Almeida reported research grants and honoraria for educational activity from Cardinal Health and EMD Serono outside the submitted work. Dr Mehanna reports research funding from AstraZeneca, Cancer Research UK, and UK National Institute for Health Research; involvement in advisory boards with Merck, Seagen, Nanobiotix, and Eisai; and equity in Docspert Health and the Warwickshire Head Neck Clinic outside the submitted work. Dr Hansen reported institutional research funding from AdvanCell, AVEO, Bristol Myers Squibb, Janssen, MacroGenics, Merck Sharp & Dohme, Roche, Seagen, and Tyra Biosciences; consulting fees from Astellas, Bayer, Eisai, Janssen, and Merck Sharp & Dohme; and travel support from Baye, outside the submitted work. No other conflicts were reported. Data Sharing Statement: See Supplement 2 . ✉ Corresponding author. Received 2024 Nov 8; Accepted 2025 Jan 28; Issue date 2025 Jun. Copyright 2025 American Medical Association. All rights reserved, including those for text and data mining, AI training, and similar technologies. PMC Copyright notice PMCID: PMC11986829 PMID: 40208586 Key Points Question Can a disease-specific health utility measure effectively discriminate between health states in patients after head and neck cancer treatment? Findings This 2-phase psychometric study developed and validated a new health utility instrument, the Head and Neck Cancer-8 dimensions (HNC-8D), based on the European Organization for Research and Treatment of Cancer’s Quality of Life Questionnaire−Core 30 and Head and Neck module 43. HNC-8D responses from 84 patients with head and neck cancer whose treatment was followed by severe conditions (eg, requiring gastrostomy tube or tracheostomy) had significantly lower scores; and predicted utilities matched observed utilities with a low mean absolute difference. Meaning The HNC-8D instrument may be a valid and accurate tool for measuring health utility after head and neck cancer treatment. Abstract Importance Generic health utility instruments lack the discriminative ability to differentiate among health states in patients after head and neck cancer treatment. Objective To develop, validate, and valuate a head and neck cancer−specific health utility measure. Design, Setting, and Participants This psychometric study comprised 2 phases to develop and validate a health utility instrument. The first phase, development and validation, occurred from January 2021 to August 2022. An expert panel selected disease-specific quality-of-life instruments as the basis for a new utility instrument. Two datasets (n = 458 and 493) were used to establish dimension structure through exploratory factor analysis, and to select items using Rasch and psychometric criteria and expert opinion. Discriminative validity of the new instrument was tested by comparing scores for different disease severities (patients with and without gastrostomy and tracheostomy tubes). The second phase, valuation, was conducted from January 2023 to January 2024 in a quaternary referral center with healthy participants. Participants completed time−trade-off exercises for 100 sampled health states and were randomized to discovery and validation sets (80:20). Using a repeated measures model, a scoring algorithm to predict utilities of health states within the instrument was created in the discovery set and tested in both sets. Data were analyzed from January 2022 to December 2023. Intervention Participants performed time−trade-off exercises for various states. Main Outcomes and Measures Discriminative validity (first phase) and the mean absolute differences of predicted and observed utilities (second phase). Results The European Organization for Research and Treatment of Cancer’s Quality of Life Questionnaire−Core 30 and its Head and Neck module 43 were selected by the expert panel and used as the basis instruments. Exploratory factor analysis established 8 dimensions, with 1 item selected per dimension. Of the 488 respondents, 84 with gastrostomy and/or tracheostomy tubes reported lower scores for 7 of the 8 items. In the second phase, 2497 valuations were performed by 250 healthy participants (mean [SD] age, 42.4 [16.5] years; 166 [66%] females). The scoring algorithm produced mean absolute differences between predicted and observed utilities of 0.041 (95% CI, 0.034-0.047) and 0.082 (95% CI, 0.065-0.100) in the discovery and validation sets, respectively. Conclusions and Relevance This psychometric study developed a new head and neck cancer-specific utility measure, the HNC-8D (Head and Neck Cancer−8 Dimensions). The instrument demonstrated predictive accuracy for measuring health utility and can be used to differentiate health utility states following head and neck cancer treatment. This psychometric study develops and validates a head and neck cancer−specific utility tool for measuring quality of life among patients after treatment. Introduction Health utility measures the value or preference that society places on a given health state, ranging from 0 (akin to death) to 1 (akin to perfect health), although states worse than death are possible. 1 These values help compare the desirability of various health states after cancer diagnosis and treatment. Although several generic instruments, such as the European Quality of Life−5 dimensions (EQ-5D) 2 and the Health Utilities Index (HUI), 3 as well as cancer-specific instruments, such as the European Organization for Research and Treatment of Cancer (EORTC) Quality of Life Utility Measure-core 10 dimensional (QLU-C10D), 4 have been developed and validated to measure both generic or cancer-specific health utility, there remains no widely accepted disease-specific health utility instrument for head and neck cancer. These instruments are used in clinical trials as surrogates for quality of life and in cost utility analyses (CUAs). With the rising costs of new cancer treatments, recommendations to adopt new therapies require demonstration of value through CUAs. 5 , 6 In CUAs, new treatments are evaluated based on their incremental (or decremental) costs, while benefits are measured with a composite end point of survival weighted by health utility, quality-adjusted life expectancy. Although disease-specific quality-of-life instruments—eg, the EORTC Head and Neck module 43 (HN43), 7 the University of Washington Quality of Life (UW-QOL), 8 and the Functional Assessment of Cancer Therapy−Head & Neck (FACT-HN) 9 —can capture quality-of-life benefits, they cannot be used as weights to calculate quality-adjusted life expectancy. However, generic utility instruments, such as the EQ-5D, may be used in CUAs but they are limited in discriminating between head and neck cancer-specific disease states. 10 For example, disease-specific quality-of-life instruments, such as the HN43, are far better at differentiating between differences in swallowing, xerostomia, and shoulder function than are generic utility instruments. 10 , 11 , 12 Therefore, use of generic utility instruments in clinical trials may not detect important disease-specific differences among treatments. We aimed to develop a new head and neck cancer−specific utility instrument using an established methodologic approach in which disease-specific health-related quality-of-life measures form the basis to develop a disease-specific utility instrument. This instrument is intended to better differentiate health states experienced by patients with head and neck cancer to measure outcomes in clinical trials. Methods This psychometric study was reviewed and approved by the institutional review board of the University Health Network (Toronto, Canada). Informed consent was obtained in writing from all participants. The study had 2 phases. The first phase, development and validation, occurred from January 2021 to August 2022, and the second, valuation, from January 2023 to January 2024. Phase 1: Instrument Development Expert Panel The study protocol and methodological framework were endorsed by the Head and Neck Cancer International Group. An expert panel was assembled with representation from surgery (J.D.A., H.M., C.S., S.R., L.K.), radiation oncology (S.P., M.T.T., J.R.), medical oncology (L.L., A.P., A.H.), and health economics (M.B.) to guide instrument development. The panel was chosen with representative participation from surgery, radiation oncology, medical oncology, and statistics. Panel members were recommended from leadership within the Head and Neck Cancer International Group. Methodological Process A previous methodological process described by Brazier et al 13 for the development of a disease-specific utility instrument was used. The process involved 4 steps: (1) selection of a disease-specific quality-of-life instrument for item derivation, (2) establishing dimension structure using exploratory factor analysis, (3) item selection and reduction, and (4) instrument validation. Selection of Quality-of-Life Instruments for Item Derivation and Datasets Four head and neck cancer−specific quality-of-life instruments—the HN43, 7 EORTC Head and Neck module 35, 14 UW-QOL, 8 and the FACT-HN 9 —were identified in a published systematic review. 15 The RAND/UCLA method 16 was used to determine the appropriateness of each instrument based on its use in the published literature, face validity, and psychometric criteria. In addition, each instrument was ranked by each expert panel member. The instrument with the highest ranked score was selected. Establishing Dimension Structure Two prospective datasets, the EORTC HN43 validation dataset (hereafter, EORTC dataset; n = 458) 7 and a prospective quality-of-life database from Brazil 17 (hereafter, Brazilian dataset; n = 493) were used for instrument development and validation. Dimension structure was established using exploratory factor analysis. Principal component analysis with varimax rotation was used to identify structurally independent dimensions. We prespecified extracting from 5 to 10 factors to avoid an unnecessarily complex instrument. Eigenvalues, scree plots, and component matrices were used to determine dimension structure. Eigenvalue cutoffs of 0.7 were used to identify principal component factors. When factor loadings in the component matrix were within 0.15 of each other (cross loading), subjective decision-making based on clinical grounds was used to attribute items to appropriate factors. Following factor analysis, the expert panel met to decide on the number of factors to retain. Item Selection and Reduction One item was retained per dimension. 13 , 18 , 19 , 20 , 21 Item selection was based on Rasch analysis as well as psychometric techniques. A Rasch partial credit model was constructed for each dimension and all items within each dimension were fitted to the model. Rasch analysis and psychometric criteria were used to select items to a single item per dimension (eMethods in Supplement 1 ). Final decision-making for item selection was based on both statistical and clinical grounds. Instrument Validation The Brazilian dataset was used for validation. Discriminative validity of the new instrument was assessed by comparing the item severity scores for patients with and without gastrostomy tubes and patients with and without tracheostomy tubes using the Cramer V effect size measure (95% CIs). 22 Using this statistic, values were considered as having the following effect size: 0 to less than 0.10, negligible; 0.10 to less than 0.20, weak; 0.20 to less than 0.40, moderate; 0.40 to less than 0.60, relatively strong; 0.60 to less than 0.80, strong; and 0.80 to 1.00, very strong. 23 Phase 2: Instrument Valuation The final version of the new instrument contained 8 items with 3 response levels per item for a total of 3 8 or 6561 health states. Response levels 3 (“quite a bit”) and 4 (“very much”) from the parent instruments were collapsed into a single-response level due to ceiling effects (<10% frequency) and to reduce the number of health state valuations. Valuations were performed for 100 different states, including 2 corner states (ie, states with response levels 11111111 and 33333333 for the 8 items) and 98 intermediate states. Of the 100 states valued, 36 were chosen based on orthogonal block design, in addition to 64 high-frequency states. Given that cost-utility studies consider the resources of society, methodologists recommend sampling preferences from the general population. 23 Therefore, we recruited members of the general public, with no prior head and neck cancer history, through hospital advertisements and by approaching patients without cancer, family members, and administrative staff. Each participant received a coffee shop gift card ($10 value) for participation. Interviews were conducted by trained interview staff. Interview staff (research assistants and medical students) were asked to review the protocol and undergo a 1-hour training session on techniques to explain the vignettes and time−trade-off (TTO) exercise. TTO was chosen over standard gamble because participants often have difficulty with comprehension of standard gamble and valuations tend to be overvalued relative to standard gamble. 24 Participant demographic characteristics were collected, including self-reported race and ethnicity. Each participant was presented with different health states (n = 8) in vignette form, characterizing varying disease severities for each of the 8 items in the instrument. Participants were then asked to complete a TTO exercise for each of the 10 different states. Participants chose between 10 years in a given health state or perfect health, iteratively reducing the years in perfect health until preferences switched. For nonswitchers, a follow-up involved comparing perfect health with a combination of perfect health and the alternative state, refining utility scores (eMethods in Supplement 1 ). Statistical Analysis Assuming that 20 valuations 25 , 26 are required for each of the 98 intermediate states valued and given that each participant would valuate 8 intermediate health states (and 2 corner states), a sample size of 245 participants was required, with an additional 5 participants to account for missing responses. Instrument scoring was analyzed based on a repeated measures model. The utility score from the TTO exercise was used as the dependent variable, while the 8 items scores were used as categorical independent variables. The main effect model had a random intercept and 16 fixed effects in which the participant was the cluster for 8 questions (2 dummy variables per question for the 3 response levels). To improve the model’s performance, we evaluated performance by additionally adding the number of level 1 and level 3 responses per respondent, and the first-order interactions between 8 questions (28 combinations) separately to the main effect model. The number of level 1 responses and a few first-order interactions were significant. We then included all of the significant terms in the main effect model and found that only the number of level 1 responses remained significant. Although this improved the model’s goodness of fit, it did not improve the mean absolute difference in predictive ability. As such, the final model excluded the number of level 1 and 3 responses and interaction terms. The total number of valuations completed by the participants were randomly split 80:20 into discovery and validation cohorts, respectively, to evaluate model performance. The model was developed in the discovery cohort. Valuations from a given participant were either included in the discovery or validation sets, but not both. Model performance was assessed using the mean absolute difference (MAD). Data analyses were performed from January 2023 to December 2023 using SAS, version 9.4 (SAS Institute Inc) and R version, 4.1.2 (The R Foundation for Statistical Computing). Results Instrument Selection Four instruments, the HN43, 7 HN35, 14 UW-QOL, 8 and the FACT-HN 9 were presented to the panel of 12 experts along with relevant information regarding content validity, usage in published literature, and psychometric properties. For the 4 instruments, 10 (83%), 9 (75%), 7 (58%), and 1 (8%) panel members scored the HN43, HN35, UW-QOL, and FACT-HN, respectively, as appropriate (ie, score 7-9 on RAND/UCLA criteria 16 ) for utility item derivation. The median (range) ranking scores for each instrument were 1 (1-2) points, 2 (1-4), 4 (1-4), and 3 (3-4) for the respective instruments, with 7 (58%) first-place rankings for the HN43. After consensus discussion, the expert panel selected the HN43 utility item selection, and the decided to add the EORTC Quality of Life Questionnaire−Core 30 (QLQ-C30) for completeness. 27 Factor Analysis Exploratory factor analysis performed on the EORTC dataset (eTable 1 in Supplement 1 ) demonstrated that a 5-dimension solution explained 48% of the variance of the 73 items, whereas a 10-dimension solution explained 59%. After confirming this dimension structure in the Brazilian dataset (eTable 2 in Supplement 1 ), the panel further explored 10 dimensions (eTable 3 in Supplement 1 ). Rasch and Psychometric Analysis and Item Selection A single item was selected per dimension using Rasch and psychometric criteria (eTable 4 in Supplement 1 ). Initially we explored 10 dimensions, but finally accepted 8. Thus, the new instrument was named the Head and Neck Cancer-8 dimensional (HNC-8D) questionnaire. “Tired” and “need to rest” (from the QLQ-C30) were considered for inclusion in the first dimension of the HNC-8D. “Tired” was selected based on face validity and because the other item had differential item functioning. In the second dimension, the panel considered “swallowing solid food” and “eating” (HN43), and selected the latter based on better coverage of the latent space and face validity for the social aspect of eating. For the third dimension, the panel discussed “dissatisfied with body,” “less attractive,” and “worries about health” (HN43), and selected the former option based on good responsiveness, although this item was limited by floor effect (46%). “Speaking clearly” was selected over “talking to people” and “talking in a noisy environment” (HN43) for the fourth dimension based on item level coverage and smaller floor effects. “Dry mouth” (HN43) was selected for the fifth dimension based on content validity and item level coverage. “Skin problems” was selected over “taste” (HN43) as the sixth dimension based on its clinical importance. Although both items capture different concepts, “taste” was excluded for ease of administration. “Coughing” (HN43) was selected for the seventh dimension due to good face validity for aspiration-related issues, although this item was associated with floor effects and low responsiveness (standardized response mean, 0.20). “Pain in shoulder” (HN43) was retained in the eighth dimension due to good item-level coverage of the latent space, but had floor effect (65%) due to a large proportion of patient treated without surgery. Two additional items or dimensions were considered but rejected by the group. “Teeth” was rejected due to high correlation with other retained items, eg, “dry mouth.” “Less interest in sex” was rejected due to differential item functioning. Construct Validation We compared item scores for each item in patients with and without gastrostomy or tracheostomy tube placement in the Brazilian dataset. Seven of the 8 item responses were weakly (skin problems and pain in shoulder) or moderately (tired, eating, dissatisfied with body, speaking clearly, coughing) associated with the presence of a feeding tube. One item (dry mouth) showed a negligible association ( Table 1 ). Similarly, 7 of the 8 items had either a weak (tired, eating, pain in shoulder, coughing, dry mouth) or moderate (dissatisfied with body, speaking clearly) association, whereas only 1 item (skin problems) had a negligible association ( Table 2 ). Table 1. Discriminative Validity of Head and Neck Cancer−8 Dimensional Instrument Comparing Item Severity Score in Patients Without and With a Gastrostomy Tube. Item Full sample (N = 488) Gastrostomy tube, No. (%) Effect size, Cramer V (95% CI) No (n = 370) Yes (n = 118) Tired Not at all 243 (50) 209 (56) 34 (29) 0.26 (0.17-0.36) A little 148 (30) 105 (28) 43 (36) Quite a bit/very much 97 (20) 56 (15) 41 (35) Eating Not at all 205 (42) 184 (50) 21 (18) 0.38 (0.30-0.47) A little 103 (21) 88 (24) 15 (13) Quite a bit/very much 180 (37) 98 (26) 82 (69) Dissatisfied with body Not at all 329 (67) 269 (73) 60 (51) 0.21 (0.13-0.32) A little 76 (16) 53 (14) 23 (19) Quite a bit/very much 83 (17) 48 (13) 35 (30) Speaking clearly Not at all 225 (46) 198 (54) 27 (23) 0.37 (0.29-0.46) A little 107 (22) 90 (24) 17 (14) Quite a bit/very much 156 (32) 82 (22) 74 (63) Skin problems Not at all 327 (67) 261 (71) 66 (56) 0.13 (0.05-0.23) A little 99 (20) 66 (18) 33 (28) Quite a bit/very much 62 (13) 43 (12) 19 (16) Pain in shoulder Not at all 325 (67) 262 (71) 63 (53) 0.18 (0.09-0.28) A little 96 (20) 69 (19) 27 (23) Quite a bit/very much 67 (14) 39 (11) 28 (24) Coughing Not at all 318 (65) 263 (71) 55 (47) 0.24 (0.15-0.34) A little 108 (22) 73 (20) 35 (30) Quite a bit/very much 62 (13) 34 (9) 28 (24) Dry mouth Not at all 133 (27) 98 (26) 35 (30) 0.03 (0.01-0.14) A little 103 (21) 80 (22) 23 (19) Quite a bit/very much 252 (52) 192 (52) 60 (51) Open in a new tab Table 2. Discriminative Validity of Head and Neck Cancer−8 Dimensional Instrument Comparing Item Severity Score in Patients Without and With a Tracheostomy Tube. Item Full sample (N = 488) Tracheostomy tube, No. (%) Effect size, Cramer V (95% CI) No (n = 404) Yes (n = 84) Tired Not at all 243 (50) 212 (52) 31 (37) 0.12 (0.05-0.21) A little 148 (30) 118 (29) 30 (36) Quite a bit/very much 97 (20) 74 (18) 23 (27) Eating Not at all 205 (42) 175 (43) 30 (36) 0.18 (0.10-0.27) A little 103 (21) 95 (24) 8 (10) Quite a bit/very much 180 (37) 134 (33) 46 (55) Dissatisfied with body Not at all 329 (67) 290 (72) 39 (46) 0.22 (0.13-0.33) A little 76 (16) 59 (15) 17 (20) Quite a bit/very much 83 (17) 55 (14) 28 (33) Speaking clearly Not at all 225 (46) 216 (53) 9 (11) 0.49 (0.41-0.57) A little 107 (22) 101 (25) 6 (7) Quite a bit/very much 156 (32) 87 (22) 69 (82) Skin problems Not at all 327 (67) 277 (69) 50 (60) 0.08 (0.02-0.17) A little 99 (20) 77 (19) 22 (26) Quite a bit/very much 62 (13) 50 (12) 12 (14) Pain in shoulder Not at all 325 (67) 284 (70) 41 (49) 0.18 (0.10-0.28) A little 96 (20) 74 (18) 22 (26) Quite a bit/very much 67 (14) 46 (11) 21 (25) Coughing Not at all 318 (65) 278 (69) 40 (48) 0.18 (0.10-0.28) A little 108 (22) 83 (21) 25 (30) Quite a bit/very much 62 (13) 43 (11) 19 (23) Dry mouth Not at all 133 (27) 96 (24) 37 (44) 0.18 (0.091-0.28) A little 103 (21) 87 (22) 16 (19) Quite a bit/very much 252 (52) 221 (55) 31 (37) Open in a new tab Instrument Valuation A total of 250 healthy participants (mean [SD] age, 42.4 [16.5] years; 166 [66%] females and 84 males [34%]; 6 Native American−Canadian [2%], 44 Asian or Pacific Islander [18%], 23 African American−Canadian [9%], 11 Hispanic or Latino [4%], 111 White [45%], and 28 individuals of another group [11%]) valued 10 states each (8 intermediate and 2 corner states; Table 3 ). One hundred different health states were valuated. Valuations were completed for a total of 2497 states, of which 1998 were intermediate states. The scoring algorithm is described in Table 4 . Model performance was very good in both discovery (MAD, 0.041; 95% CI, 0.034-0.047) and validation cohorts (MAD, 0.082; 95% CI, 0.065-0.100; Figure ). Table 3. Demographic Characteristics of Participants for Item Valuation. Characteristic No. (%) Participants, No. 250 Age, y Mean (SD) 42.4 (16.5) Median (IQR) 39 (28-55) Missing data 11 Sex Female 166 (66) Male 84 (34) Highest education level College 99 (40) <High school 10 (4) High school 23 (9) Postgraduate 118 (47) Race and ethnicity Native American (Canadian) 6 (2) Asian/Pacific Islander 44 (18) African American (Canadian) 23 (9) Hispanic 11 (4) Indian/South Asian 23 (9) White 111 (45) Other a 28 (11) Missing data 4 Marital status Married 114 (46) Never married 112 (45) Separated/divorced 18 (7) Widowed 6 (2) Income bracket, $/y <20 000 37 (15) 20 000-40 000 22 (9) 40 000-60 000 48 (19) 60 000-80 000 38 (15) 80 000-100 000 37 (15) 100 000-250 000 57 (23) >250 000 9 (4) Missing data 2 Open in a new tab a Includes any race or ethnicity other than those listed. Table 4. Head and Neck Cancer−8 Dimensional Instrument and Scoring Algorithm. Question No. EORTC parent tool (item) Item Incremental utility from worst health state (3333333; U = 0.2862 a ) Level 3 (quite a bit/very much) Level 2 (a little) Level 1 (not at all) 1 QLQ-C30 (18) Were you tired? NR 0.05430 0.08345 2 HN43 (51) Have you had problems eating? NR 0.09179 0.1189 3 HN43 (50) Have you felt dissatisfied with your body? NR 0.02453 0.04629 4 HN43 (58) Have you had problems speaking clearly? NR 0.06604 0.1082 5 HN43 (42) Have you had a dry mouth? NR 0.03115 0.06564 6 HN43 (65) Have you had skin problems? (eg, itchy, dry)? NR 0.02621 0.04813 7 HN43 (46) Have you had problems coughing? NR 0.04404 0.08405 8 HN43 (63) Have you had pain in your shoulder? NR 0.08069 0.1083 Open in a new tab Abbreviations: EORTC, European Organization for Research and Treatment of Cancer; HN43, Head and Neck Cancer module 43; NR, not reported; QLQ-C30, Quality of Life Questionnaire−Core 30. a Baseline health utility score ( U ) for worst health state = 0.2862. Figure. Mean Observed and Predicted Utility for 100 Health States Sampled in Developing the Head and Neck Cancer−8 Dimensional Instrument. Open in a new tab Discussion We developed, validated, and valuated a new disease-specific utility instrument for patients with head and neck cancers. As the costs of novel therapeutics continue to rise, trials groups are assessing the economic implications of new treatment strategies by collecting health utility and performing cost-utility analyses. Without sensitive tools that have discriminative ability to detect changes in quality of life, these analyses are unlikely to capture the total health benefits and/or (un)affordability of new treatments. The HNC-8D is a head and neck cancer health utility measure derived from set of a validated and responsive disease-specific health related quality of life measure, the EORTC’s QLQ-C30 and HN43. The new instrument is designed to capture and measure important health utility changes following treatments for head and neck cancers. The HNC-8D includes 8 dimensions, 7 of which are specific to head and neck cancer and 1 generic cancer dimension. Disease-specific instruments have been shown to have better discriminative ability than generic instruments for patients with head and neck cancer. A recent study 10 used the UW-QOL and the EQ-5D to compare the effect sizes for swallowing problems between patients with severe vs mild issues. The effect size on the UW-QOL was more than 3 times larger than that measured by the EQ-5D. Similarly, the effect size comparing severe and mild xerostomia was almost twice as large on the UW-QOL compared to the EQ-5D. Other critical differences between patients who did and did not use gastrostomy tubes as well as those with severe and mild speaking problems were better captured using the UW-QOL compared to the EQ-5D. The HNC-8D contains important dimensions for dry mouth, eating, speaking clearly, and coughing, all of which have important health state utility implications and are not captured in generic instruments. Some novel treatment strategies for both the previously untreated and recurrent metastatic head and neck cancers are associated with substantial treatment-related costs. The costs of novel drug treatments and new surgical and radiotherapy technologies may overwhelm resource-constrained health care systems. In these instances, cost-utility analyses can provide a standardized reference comparison of new treatment strategies to standard of care using a metric known as incremental cost utility ratio. Policymakers and payers may defer to these analyses for funding decisions, and can be guided by the recommendation that new treatment strategies should cost less than $100 000 per quality-adjusted life-year to be considered cost-effective in North America, although these thresholds vary by jurisdiction. 6 Important treatments, such as nivolumab 28 , 29 and pembrolizumab, 30 for recurrent or metastatic head and neck cancer, or transoral robotic surgery for oropharyngeal cancers, 31 may not be considered to be cost-effective. However, these results are sensitive to the utility estimates used, and more sensitive instruments may support different conclusions. 32 , 33 , 34 Many recent phase 3 randomized clinical trials in head and neck cancer aimed at ameliorating the toxic effects of treatment (RTOG-1016, 35 De-Escalate, 36 and PET-Neck 37 ) have used a generic utility measure, the EQ-5D, for measuring health utility benefit. In trials with a particular emphasis on treatment toxic-effects amelioration, in which the health utility benefits are more likely to be disease-specific, a generic instrument may underestimate the benefit, and thus, overestimate the incremental cost utility ratio. These estimates of cost utility may have implications on recommendations by policymakers for widescale implementation and by payers for reimbursement of new treatments. The HNC-8D was valuated in a sample in Canada. The participants represent diverse backgrounds, but further valuation studies are required in other countries to account for cross-cultural differences. The current study demonstrated excellent predictive ability for health utilities. In a large valuation study of the EQ-5D 3 level (EQ-5D-3L) with 2997 patients, 38 the MAD between actual and predicted utility scores was 0.03, while in our study the MAD was 0.04 in the discovery cohort and 0.08 in the validation cohort. In the previous study, 38 utility scores for the EQ-5D-3L were estimated by sampling 42 of 243 (3 5 ) states. More recently, the European Quality of Life group developed a standard protocol 39 for valuation of the EQ-5D 5 level using a sample of 86 of 3125 (5 5 ) health states. Methodologic studies suggest that valuation of a fewer amount of states, even as few as 25, do not adversely affect predictive accuracy. 40 In the present study, we chose 100 states to model the 6561 different states of the instrument. The choice of sampling produced excellent predictive accuracy. Limitations The current study has limitations and further work is needed. Valuation studies in other countries, as well as more diverse socioeconomic populations, are required to establish generalizability and cross-cultural validity. A notable caveat of using a head and neck cancer−specific instrument is the lack of comparability across other cancers and disease entities; however, the HNC-8D can be used to differentiate among different health state severity levels after head and neck cancer treatment. Conclusions This psychometric study found that the HNC-8D may be a valid head and cancer−specific utility instrument. The HNC-8D demonstrated discriminative validity and good predictive ability for estimating health utility after head and neck cancer treatment. Future trials are needed to co-administer the HNC-8D and a generic utility instrument, such as the EQ-5D, to compare discriminative abilities and to likely confirm the superior performance of the HNC-8D. Supplement 1. eMethods. (1) Rasch and psychometric criteria for item reduction within each dimension and (2) Time trade-off methods eTable 1. EORTC dataset−the first post-treatment time point was used for instrument development (n=458). eTable 2. Brazilian dataset of 493 patients using post-treatment time point for instrument validation. eTable 3. Factor analysis and correlation coefficients (multiplied by factor of 100) for individual items to dimension (factor) for initial 10 dimension instrument eTable 4. Item selection based on Rasch and psychometric criteria jamaotolaryngolheadnecksurg-e250160-s001.pdf (597.1KB, pdf) Supplement 2. Data Sharing Statement jamaotolaryngolheadnecksurg-e250160-s002.pdf (17.8KB, pdf) References 1. Gold MR, Siegel JE, Russell LB, et al. , eds. Cost-Effectiveness in Health and Medicine. Oxford University Press; 1996. doi: 10.1093/oso/9780195108248.001.0001 [ DOI ] [ Google Scholar ] 2. Herdman M, Gudex C, Lloyd A, et al. Development and preliminary testing of the new five-level version of EQ-5D (EQ-5D-5L). Qual Life Res. 2011;20(10):1727-1736. doi: 10.1007/s11136-011-9903-x [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 3. Horsman J, Furlong W, Feeny D, Torrance G. 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Factor analysis and correlation coefficients (multiplied by factor of 100) for individual items to dimension (factor) for initial 10 dimension instrument eTable 4. Item selection based on Rasch and psychometric criteria jamaotolaryngolheadnecksurg-e250160-s001.pdf (597.1KB, pdf) Supplement 2. Data Sharing Statement jamaotolaryngolheadnecksurg-e250160-s002.pdf (17.8KB, pdf) Articles from JAMA Otolaryngology-- Head & Neck Surgery are provided here courtesy of American Medical Association ACTIONS View on publisher site Cite Collections Permalink PERMALINK Copy RESOURCES Similar articles Cited by other articles Links to NCBI Databases Cite Copy Download .nbib .nbib Format: AMA APA MLA NLM Add to Collections Create a new collection Add to an existing collection Name your collection * Choose a collection Unable to load your collection due to an error Please try again Add Cancel Follow NCBI NCBI on X (formerly known as Twitter) NCBI on Facebook NCBI on LinkedIn NCBI on GitHub NCBI RSS feed Connect with NLM NLM on X (formerly known as Twitter) NLM on Facebook NLM on YouTube National Library of Medicine 8600 Rockville Pike Bethesda, MD 20894 Web Policies FOIA HHS Vulnerability Disclosure Help Accessibility Careers NLM NIH HHS USA.gov Back to Top