R-S-S Database of Published Mapping Algorithms
At R-S-S we conducted a systematic literature review (SLR) of published mapping algorithms across multiple disease areas used to predict EQ-5D (5L or 3L) utilities. From this we created a classification system that categorises the performance of these mapping algorithms.
Use the table below to find the right mapping algorithm for you with easy-to-use filters for disease areas and classifications to quickly and easily find what you’re looking for.
| Publication Year | Reference | Mapping From | Mapping To | Disease Area | Reported R-Squared | Reported RMSE | % RMSE simulations <= mean (mu) | % R-Squared simulations >= mean (mu) | Classification |
|---|---|---|---|---|---|---|---|---|---|
| 2023 | Xie, S., Wu, J., & Chen, G. (2023). Comparative performance and mapping algorithms between EQ-5D-5L and SF-6Dv2 among the Chinese general population. The European journal of health economics : HEPAC : health economics in prevention and care, 10.1007/s10198-023-01566-x. Advance online publication. https://doi.org/10.1007/s10198-023-01566-x | SF-6Dv2 | EQ-5D-5L | Mixed Disease Types | 0.435 | 0.005 | 80.57% | 30.63% | Useful/Caution (Amber) |
| 2023 | Xie, S., Wu, J., & Chen, G. (2023). Comparative performance and mapping algorithms between EQ-5D-5L and SF-6Dv2 among the Chinese general population. The European journal of health economics : HEPAC : health economics in prevention and care, 10.1007/s10198-023-01566-x. Advance online publication. https://doi.org/10.1007/s10198-023-01566-x | EQ-5D-5L | SF-6D-v2 | Mixed Disease Types | 0.362 | 0.013 | 80.36% | 18.62% | Useful/Caution (Amber) |
| 2023 | Wan, C., Wang, Q., Xu, Z., Huang, Y., & Xi, X. (2023). Mapping health assessment questionnaire disability index onto EQ-5D-5L in China. Frontiers in public health, 11, 1123552. https://doi.org/10.3389/fpubh.2023.1123552 | HAQ-DI | EQ-5D-5L | Rheumatology | 0.513 | 0.164 | 24.00% | 42.59% | Poor (Red) |
| 2023 | Steiner, I. M., Bokemeyer, B., & Stargardt, T. (2023). Mapping from SIBDQ to EQ-5D-5L for patients with inflammatory bowel disease. The European journal of health economics : HEPAC : health economics in prevention and care, 10.1007/s10198-023-01603-9. Advance online publication. https://doi.org/10.1007/s10198-023-01603-9 | SIBDQ | EQ-5D-5L | Stomach & Bowel | 0.466 | 0.016 | 79.24% | 35.05% | Useful/Caution (Amber) |
| 2023 | Seow, L. S. E., Lau, J. H., Abdin, E., Verma, S. K., Tan, K. B., & Subramaniam, M. (2023). Mapping the schizophrenia quality of life scale to EQ-5D, HUI3 and SF-6D utility scores in patients with schizophrenia. Expert review of pharmacoeconomics & outcomes research, 23(7), 813–821. https://doi.org/10.1080/14737167.2023.2215430 | SQLS (Schizophrenia Quality of Life Scale) | SF-6D | Mental Health | 0.547 | 0.092 | 52.46% | 48.02% | Poor (Red) |
| 2023 | Seow, L. S. E., Lau, J. H., Abdin, E., Verma, S. K., Tan, K. B., & Subramaniam, M. (2023). Mapping the schizophrenia quality of life scale to EQ-5D, HUI3 and SF-6D utility scores in patients with schizophrenia. Expert review of pharmacoeconomics & outcomes research, 23(7), 813–821. https://doi.org/10.1080/14737167.2023.2215430 | SQLS (Schizophrenia Quality of Life Scale) | HUI3 | Mental Health | 0.463 | 0.228 | 7.33% | 34.09% | Poor (Red) |
| 2023 | Seow, L. S. E., Lau, J. H., Abdin, E., Verma, S. K., Tan, K. B., & Subramaniam, M. (2023). Mapping the schizophrenia quality of life scale to EQ-5D, HUI3 and SF-6D utility scores in patients with schizophrenia. Expert review of pharmacoeconomics & outcomes research, 23(7), 813–821. https://doi.org/10.1080/14737167.2023.2215430 | SQLS (Schizophrenia Quality of Life Scale) | EQ-5D-5L | Mental Health | 0.490 | 0.158 | 25.57% | 38.94% | Poor (Red) |
| 2023 | Seow, L. S. E., Lau, J. H., Abdin, E., Verma, S. K., Tan, K. B., & Subramaniam, M. (2023). Mapping the schizophrenia quality of life scale to EQ-5D, HUI3 and SF-6D utility scores in patients with schizophrenia. Expert review of pharmacoeconomics & outcomes research, 23(7), 813–821. https://doi.org/10.1080/14737167.2023.2215430 | SQLS (Schizophrenia Quality of Life Scale) | EQ-5D-3L | Mental Health | 0.522 | 0.208 | 10.10% | 43.58% | Poor (Red) |
| 2023 | Chalet, F. X., Bujaroska, T., Germeni, E., Ghandri, N., Maddalena, E. T., Modi, K., Olopoenia, A., Thompson, J., Togninalli, M., & Briggs, A. H. (2023). Mapping the Insomnia Severity Index Instrument to EQ-5D Health State Utilities: A United Kingdom Perspective. PharmacoEconomics - open, 7(1), 149–161. https://doi.org/10.1007/s41669-023-00388-0 | ISI | EQ-5D-3L | Sleep Disorder | 0.320 | 0.035 | 73.32% | 15.52% | Useful/Caution (Amber) |
| 2023 | Ben, Â. J., Pellekooren, S., Bosmans, J. E., Ostelo, R. W. J. G., Maas, E. T., El Alili, M., van Tulder, M. W., Huygen, F. J. P. M., Oosterhuis, T., Apeldoorn, A. T., van Hooff, M. L., & van Dongen, J. M. (2023). Mapping Oswestry Disability Index Responses to EQ-5D-3L Utility Values: Are Cost-Utility Results Valid?. Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research, 26(6), 873–882. https://doi.org/10.1016/j.jval.2023.01.020 | Oswestry Disability Index | EQ-5D-3L | Musculoskeletal | 0.430 | 0.220 | 8.08% | 28.51% | Poor (Red) |
| 2022 | Xu, R. H., Dong, D., Luo, N., Wong, E. L., Yang, R., Liu, J., Yuan, H., & Zhang, S. (2022). Mapping the Haem-A-QoL to the EQ-5D-5L in patients with hemophilia. Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation, 31(5), 1533–1544. https://doi.org/10.1007/s11136-021-03051-5 | Haem-A-Qol | EQ-5D-5L | Hematology | 0.503 | 0.229 | 6.16% | 41.89% | Poor (Red) |
| 2022 | Thankappan, K., Patel, T., Ajithkumar, K. K., Balasubramanian, D., Raj, M., Subramanian, S., & Iyer, S. (2022). Mapping of head and neck cancer patient concerns inventory scores on to Euroqol-Five Dimensions-Five Levels (EQ-5D-5L) health utility scores. The European journal of health economics : HEPAC : health economics in prevention and care, 23(2), 225–235. https://doi.org/10.1007/s10198-021-01369-y | Head and Neck Patient Concerns Inventory | EQ-5D-5L | Oncology | 0.280 | 0.015 | 79.03% | 11.87% | Useful/Caution (Amber) |
| 2022 | Sturkenboom, R., Keszthelyi, D., Brandts, L., Weerts, Z. Z. R. M., Snijkers, J. T. W., Masclee, A. A. M., & Essers, B. A. B. (2022). The estimation of a preference-based single index for the IBS-QoL by mapping to the EQ-5D-5L in patients with irritable bowel syndrome. Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation, 31(4), 1209–1221. https://doi.org/10.1007/s11136-021-02995-y | IBS-QoL | EQ-5D-5L | Stomach & Bowel | 0.306 | 0.166 | 23.34% | 14.26% | Poor (Red) |
| 2022 | Rencz, F., Ruzsa, G., Bató, A., Yang, Z., Finch, A. P., & Brodszky, V. (2022). Value Set for the EQ-5D-Y-3L in Hungary. PharmacoEconomics, 40(Suppl 2), 205–215. https://doi.org/10.1007/s40273-022-01190-2 | DCE | cTTO | Mixed Disease Types | 0.996 | 0.002 | 82.78% | 96.27% | Ready to Use (Green) |
| 2022 | Khan, K., Mistry, H., Matharu, M., Norman, C., Petrou, S., Stewart, K., Underwood, M., & Achana, F. (2022). Mapping between headache specific and generic preference-based health-related quality of life measures. BMC medical research methodology, 22(1), 277. https://doi.org/10.1186/s12874-022-01762-y | HIT-6 | SF-6D | Neurology | 0.238 | 0.010 | 80.46% | 8.48% | Useful/Caution (Amber) |
| 2022 | Khan, K., Mistry, H., Matharu, M., Norman, C., Petrou, S., Stewart, K., Underwood, M., & Achana, F. (2022). Mapping between headache specific and generic preference-based health-related quality of life measures. BMC medical research methodology, 22(1), 277. https://doi.org/10.1186/s12874-022-01762-y | HIT-6 | EQ-5D-5L | Neurology | 0.093 | 0.055 | 66.63% | 2.85% | Useful/Caution (Amber) |
| 2022 | Khan, K., Mistry, H., Matharu, M., Norman, C., Petrou, S., Stewart, K., Underwood, M., & Achana, F. (2022). Mapping between headache specific and generic preference-based health-related quality of life measures. BMC medical research methodology, 22(1), 277. https://doi.org/10.1186/s12874-022-01762-y | CH-QLQ | SF-6D | Neurology | 0.527 | 0.009 | 80.53% | 45.47% | Useful/Caution (Amber) |
| 2022 | Khan, K., Mistry, H., Matharu, M., Norman, C., Petrou, S., Stewart, K., Underwood, M., & Achana, F. (2022). Mapping between headache specific and generic preference-based health-related quality of life measures. BMC medical research methodology, 22(1), 277. https://doi.org/10.1186/s12874-022-01762-y | CH-QLQ | EQ-5D-5L | Neurology | 0.293 | 0.058 | 65.55% | 11.49% | Useful/Caution (Amber) |
| 2022 | Kangwanrattanakul K. (2023). Mapping of the World Health Organization Quality of Life Brief (WHOQOL-BREF) to the EQ-5D-5L in the General Thai Population. PharmacoEconomics - open, 7(1), 139–148. https://doi.org/10.1007/s41669-022-00380-0 | WHOQOL-BREF | EQ-5D-5L | Mixed Disease Types | 0.223 | 0.008 | 80.38% | 7.57% | Useful/Caution (Amber) |
| 2022 | He, Z., Liang, W., Xu, W., Huang, W., Wang, X., Huang, K., & Yang, L. (2022). Mapping the FACT-G to EQ-5D-3L utility index in cancer with the Chinese values set. Expert review of pharmacoeconomics & outcomes research, 22(7), 1103–1116. https://doi.org/10.1080/14737167.2022.2091546 | FACT-G | EQ-5D-3L | Oncology | 0.623 | 0.062 | 64.53% | 62.46% | Ready to Use (Green) |
| 2021 | Yousefi, M., Nahvijou, A., Sari, A. A., & Ameri, H. (2021). Mapping QLQ-C30 Onto EQ-5D-5L and SF-6D-V2 in Patients With Colorectal and Breast Cancer From a Developing Country. Value in health regional issues, 24, 57–66. https://doi.org/10.1016/j.vhri.2020.06.007 | EORTC QLQ-C30 | SF-6D-v2 | Oncology | 0.749 | 0.062 | 64.16% | 80.38% | Ready to Use (Green) |
| 2021 | Yousefi, M., Nahvijou, A., Sari, A. A., & Ameri, H. (2021). Mapping QLQ-C30 Onto EQ-5D-5L and SF-6D-V2 in Patients With Colorectal and Breast Cancer From a Developing Country. Value in health regional issues, 24, 57–66. https://doi.org/10.1016/j.vhri.2020.06.006 | EORTC QLQ-C30 | EQ-5D-5L | Oncology | 0.719 | 0.100 | 49.11% | 76.60% | Useful/Caution (Amber) |
| 2021 | Wang, K., Guo, X., Yu, S., Gao, L., Wang, Z., Zhu, H., Xing, B., Zhang, S., & Dong, D. (2021). Mapping of the acromegaly quality of life questionnaire to ED-5D-5L index score among patients with acromegaly. The European journal of health economics : HEPAC : health economics in prevention and care, 22(9), 1381–1391. https://doi.org/10.1007/s10198-021-01318-9 | AcroQoL | EQ-5D-5L | Endochrine disorder | 0.458 | 0.109 | 46.30% | 33.14% | Poor (Red) |
| 2021 | Sakthong P. (2021). Mapping World Health Organization Quality of Life-BREF Onto 5-Level EQ-5D in Thai Patients With Chronic Diseases. Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research, 24(8), 1089–1094. https://doi.org/10.1016/j.jval.2021.03.001 | WHOQOL-BREF | EQ-5D-5L | Chronic Disease | 0.480 | 0.013 | 79.79% | 37.15% | Useful/Caution (Amber) |
| 2021 | Oliveira Gonçalves, A. S., Panteli, D., Neeb, L., Kurth, T., & Aigner, A. (2022). HIT-6 and EQ-5D-5L in patients with migraine: assessment of common latent constructs and development of a mapping algorithm. The European journal of health economics : HEPAC : health economics in prevention and care, 23(1), 47–57. https://doi.org/10.1007/s10198-021-01342-9 | HIT-6 | EQ-5D | Neurology | 0.294 | 0.197 | 13.46% | 12.38% | Poor (Red) |
| 2021 | Ahadi, M. S., Vahidpour, N., Togha, M., Daroudi, R., Nadjafi-Semnani, F., Mohammadshirazi, Z., Akbari-Sari, A., & Ghorbani, Z. (2021). Assessment of Utility in Migraine: Mapping the Migraine-Specific Questionnaire to the EQ-5D-5L. Value in health regional issues, 25, 57–63. https://doi.org/10.1016/j.vhri.2020.12.003 | MSQ | EQ-5D-5L | Neurology | 0.316 | 0.230 | 6.39% | 14.99% | Poor (Red) |
| 2020 | Yfantopoulos, J., & Chantzaras, A. (2020). Health-related quality of life and health utilities in insulin-treated type 2 diabetes: the impact of related comorbidities/complications. The European journal of health economics : HEPAC : health economics in prevention and care, 21(5), 729–743. https://doi.org/10.1007/s10198-020-01167-y | HRQoL | EQ-5D-5L | Endochrine disorder | 0.215 | 0.229 | 6.73% | 7.04% | Poor (Red) |
| 2020 | Xu, R. H., Wong, E. L. Y., Jin, J., Dou, Y., & Dong, D. (2020). Mapping of the EORTC QLQ-C30 to EQ-5D-5L index in patients with lymphomas. The European journal of health economics : HEPAC : health economics in prevention and care, 21(9), 1363–1373. https://doi.org/10.1007/s10198-020-01220-w | EORTC QLQ-C30 | EQ-5D-5L | Oncology | 0.605 | 0.128 | 37.62% | 58.46% | Useful/Caution (Amber) |
| 2020 | Noel, C. W., Stephens, R. F., Su, J. S., Xu, W., Krahn, M., Monteiro, E., Goldstein, D. P., Giuliani, M., Hansen, A. R., & de Almeida, J. R. (2020). Mapping the EORTC QLQ-C30 and QLQ-H&N35, onto EQ-5D-5L and HUI-3 indices in patients with head and neck cancer. Head & neck, 42(9), 2277–2286. https://doi.org/10.1002/hed.26182 | EORTC QLQ-C30 & QLQ-H&N35 | HUI3 | Oncology | 0.575 | 0.168 | 21.21% | 52.78% | Useful/Caution (Amber) |
| 2020 | Noel, C. W., Stephens, R. F., Su, J. S., Xu, W., Krahn, M., Monteiro, E., Goldstein, D. P., Giuliani, M., Hansen, A. R., & de Almeida, J. R. (2020). Mapping the EORTC QLQ-C30 and QLQ-H&N35, onto EQ-5D-5L and HUI-3 indices in patients with head and neck cancer. Head & neck, 42(9), 2277–2286. https://doi.org/10.1002/hed.26181 | EORTC QLQ-C30 & QLQ-H&N35 | EQ-5D | Oncology | 0.750 | 0.064 | 63.77% | 79.96% | Ready to Use (Green) |
| 2020 | Martín-Fernández, J., Morey-Montalvo, M., Tomás-García, N., Martín-Ramos, E., Muñoz-García, J. C., Polentinos-Castro, E., Rodríguez-Martínez, G., Arenaza, J. C., García-Pérez, L., Magdalena-Armas, L., & Bilbao, A. (2020). Mapping analysis to predict EQ-5D-5 L utility values based on the Oxford Hip Score (OHS) and Oxford Knee Score (OKS) questionnaires in the Spanish population suffering from lower limb osteoarthritis. Health and quality of life outcomes, 18(1), 184. https://doi.org/10.1186/s12955-020-01435-8 | OKS | EQ-5D-5L | Musculoskeletal | 0.606 | 0.027 | 75.47% | 59.96% | Ready to Use (Green) |
| 2020 | Martín-Fernández, J., Morey-Montalvo, M., Tomás-García, N., Martín-Ramos, E., Muñoz-García, J. C., Polentinos-Castro, E., Rodríguez-Martínez, G., Arenaza, J. C., García-Pérez, L., Magdalena-Armas, L., & Bilbao, A. (2020). Mapping analysis to predict EQ-5D-5 L utility values based on the Oxford Hip Score (OHS) and Oxford Knee Score (OKS) questionnaires in the Spanish population suffering from lower limb osteoarthritis. Health and quality of life outcomes, 18(1), 184. https://doi.org/10.1186/s12955-020-01435-8 | OHS | EQ-5D-5L | Musculoskeletal | 0.715 | 0.022 | 77.33% | 75.77% | Ready to Use (Green) |
| 2020 | Lamu A. N. (2020). Does linear equating improve prediction in mapping? Crosswalking MacNew onto EQ-5D-5L value sets. The European journal of health economics : HEPAC : health economics in prevention and care, 21(6), 903–915. https://doi.org/10.1007/s10198-020-01183-y | MacNew | EQ-5D-5L | Cardiovascular | 0.596 | 0.123 | 38.95% | 57.05% | Useful/Caution (Amber) |
| 2020 | Klapproth, C. P., van Bebber, J., Sidey-Gibbons, C. J., Valderas, J. M., Leplege, A., Rose, M., & Fischer, F. (2020). Predicting EQ-5D-5L crosswalk from the PROMIS-29 profile for the United Kingdom, France, and Germany. Health and quality of life outcomes, 18(1), 389. https://doi.org/10.1186/s12955-020-01629-0 | PROMIS-29 | EQ-5D-5L | Mixed Disease Types | 0.740 | 0.076 | 59.57% | 78.70% | Ready to Use (Green) |
| 2020 | Klapproth, C. P., van Bebber, J., Sidey-Gibbons, C. J., Valderas, J. M., Leplege, A., Rose, M., & Fischer, F. (2020). Predicting EQ-5D-5L crosswalk from the PROMIS-29 profile for the United Kingdom, France, and Germany. Health and quality of life outcomes, 18(1), 389. https://doi.org/10.1186/s12955-020-01629-0 | PROMIS-29 | EQ-5D-5L | Mixed Disease Types | 0.720 | 0.075 | 59.05% | 76.34% | Ready to Use (Green) |
| 2020 | Klapproth, C. P., van Bebber, J., Sidey-Gibbons, C. J., Valderas, J. M., Leplege, A., Rose, M., & Fischer, F. (2020). Predicting EQ-5D-5L crosswalk from the PROMIS-29 profile for the United Kingdom, France, and Germany. Health and quality of life outcomes, 18(1), 389. https://doi.org/10.1186/s12955-020-01629-0 | PROMIS-29 | EQ-5D-5L | Mixed Disease Types | 0.640 | 0.079 | 57.14% | 63.83% | Ready to Use (Green) |
| 2020 | Juan Su, Tong Liu, Shunping Li, Yue Zhao & Yehong Kuang (2020) A mapping study in mainland China: predicting EQ-5D-5L utility scores from the psoriasis disability index, Journal of Medical Economics, 23:7, 737-743, DOI: 10.1080/13696998.2020.1748636 | PDI | EQ-5D-5L | Dermatology | 0.231 | 0.133 | 34.78% | 8.04% | Poor (Red) |
| 2020 | Hunger, M., Eriksson, J., Regnier, S. A., Mori, K., Spertus, J. A., & Cristino, J. (2020). Mapping the Kansas City Cardiomyopathy Questionnaire (KCCQ) Onto EQ-5D-3L in Heart Failure Patients: Results for the Japanese and UK Value Sets. MDM policy & practice, 5(2), 2381468320971606. https://doi.org/10.1177/2381468320971606 | KCCQ | EQ-5D-3L | Cardiovascular | 0.489 | 0.121 | 40.64% | 38.48% | Poor (Red) |
| 2020 | Bilbao, A., Martín-Fernández, J., García-Pérez, L., Arenaza, J. C., Ariza-Cardiel, G., Ramallo-Fariña, Y., & Ansola, L. (2020). Mapping WOMAC Onto the EQ-5D-5L Utility Index in Patients With Hip or Knee Osteoarthritis. Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research, 23(3), 379–387. https://doi.org/10.1016/j.jval.2019.09.2755 | WOMAC | EQ-5D-5L | Musculoskeletal | 0.620 | 0.177 | 18.63% | 62.45% | Useful/Caution (Amber) |
| 2020 | Ameri, H., Yousefi, M., Yaseri, M., Nahvijou, A., Arab, M., & Akbari Sari, A. (2020). Mapping EORTC-QLQ-C30 and QLQ-CR29 onto EQ-5D-5L in Colorectal Cancer Patients. Journal of gastrointestinal cancer, 51(1), 196–203. https://doi.org/10.1007/s12029-019-00229-7 | QLQ-CR29 | EQ-5D-5L | Oncology | 0.484 | 0.130 | 36.16% | 38.37% | Poor (Red) |
| 2020 | Ameri, H., Yousefi, M., Yaseri, M., Nahvijou, A., Arab, M., & Akbari Sari, A. (2020). Mapping EORTC-QLQ-C30 and QLQ-CR29 onto EQ-5D-5L in Colorectal Cancer Patients. Journal of gastrointestinal cancer, 51(1), 196–203. https://doi.org/10.1007/s12029-019-00229-6 | EORTC QLQ-C30 | EQ-5D-5L | Oncology | 0.677 | 0.102 | 48.67% | 69.39% | Useful/Caution (Amber) |
| 2019 | Yang, Q., Yu, X. X., Zhang, W., & Li, H. (2019). Mapping function from FACT-B to EQ-5D-5 L using multiple modelling approaches: data from breast cancer patients in China. Health and quality of life outcomes, 17(1), 153. https://doi.org/10.1186/s12955-019-1224-8 | FACT-B | EQ-5D-5L | Oncology | 0.690 | 0.106 | 46.94% | 72.18% | Useful/Caution (Amber) |
| 2019 | Stephens, R. F., Noel, C. W., Su, J. S., Xu, W., Krahn, M., Monteiro, E., Goldstein, D. P., Giuliani, M., Hansen, A. R., & de Almeida, J. R. (2020). Mapping the University of Washington Quality of life questionnaire onto EQ-5D and HUI-3 indices in patients with head and neck cancer. Head & neck, 42(3), 513–521. https://doi.org/10.1002/hed.26032 | UW QOL | HUI3 | Oncology | 0.366 | 0.200 | 12.47% | 19.99% | Poor (Red) |
| 2019 | Stephens, R. F., Noel, C. W., Su, J. S., Xu, W., Krahn, M., Monteiro, E., Goldstein, D. P., Giuliani, M., Hansen, A. R., & de Almeida, J. R. (2020). Mapping the University of Washington Quality of life questionnaire onto EQ-5D and HUI-3 indices in patients with head and neck cancer. Head & neck, 42(3), 513–521. https://doi.org/10.1002/hed.26031 | UW QOL | EQ-5D | Oncology | 0.628 | 0.076 | 58.49% | 63.35% | Ready to Use (Green) |
| 2019 | Shi, Y., Thompson, J., Walker, A. S., Paton, N. I., Cheung, Y. B., & EARNEST Trial Team (2019). Mapping the medical outcomes study HIV health survey (MOS-HIV) to the EuroQoL 5 Dimension (EQ-5D-3 L) utility index. Health and quality of life outcomes, 17(1), 83. https://doi.org/10.1186/s12955-019-1135-8 | MOS-HIV | EQ-5D-3L | Autoimmune | 0.542 | 0.038 | 72.35% | 48.05% | Useful/Caution (Amber) |
| 2019 | Panchagnula, S., Sun, X., Montejo, J. D., Nouri, A., Kolb, L., Virojanapa, J., Camara-Quintana, J. Q., Sommaruga, S., Patel, K., Lakomkin, N., Abbed, K., & Cheng, J. S. (2019). Validating the Transformation of PROMIS-GH to EQ-5D in Adult Spine Patients. Journal of clinical medicine, 8(10), 1506. https://doi.org/10.3390/jcm8101506 | PROMIS-GHS | EQ-5D | Musculoskeletal | 0.590 | 0.020 | 76.83% | 54.87% | Ready to Use (Green) |
| 2019 | Gärtner, F. R., Marinus, J., van den Hout, W. B., Vleggeert-Lankamp, C., & Stiggelbout, A. M. (2020). The Cervical Radiculopathy Impact Scale: development and evaluation of a new functional outcome measure for cervical radicular syndrome. Disability and rehabilitation, 42(13), 1894–1905. https://doi.org/10.1080/09638288.2018.1534996 | Cervical Radiculopathy Impact Scale | EQ-5D | Neurology | 0.530 | 0.198 | 12.96% | 45.15% | Poor (Red) |
| 2019 | Goodwin, E., Hawton, A., & Green, C. (2019). Using the Fatigue Severity Scale to inform healthcare decision-making in multiple sclerosis: mapping to three quality-adjusted life-year measures (EQ-5D-3L, SF-6D, MSIS-8D). Health and quality of life outcomes, 17(1), 136. https://doi.org/10.1186/s12955-019-1205-y | Fatigue severity scale | SF-6D | CNS | 0.451 | 0.100 | 49.34% | 32.32% | Poor (Red) |
| 2019 | Beck, A. C. C., Kieffer, J. M., Retèl, V. P., van Overveld, L. F. J., Takes, R. P., van den Brekel, M. W. M., van Harten, W. H., & Stuiver, M. M. (2019). Mapping the EORTC QLQ-C30 and QLQ-H&N35 to the EQ-5D for head and neck cancer: Can disease-specific utilities be obtained?. PloS one, 14(12), e0226077. https://doi.org/10.1371/journal.pone.0226077 | EORTC QLQ-C30 | EQ-5D | Oncology | 0.388 | 0.104 | 46.64% | 23.15% | Poor (Red) |
| 2018 | Xavier Badia, Peter Trainer, Nienke R. Biermasz, Jitske Tiemensma, Agata Carreño, Montse Roset, Anna Forsythe & Susan M. Webb (2018) Mapping AcroQoL scores to EQ-5D to obtain utility values for patients with acromegaly, Journal of Medical Economics, 21:4, 382-389, DOI: 10.1080/13696998.2017.1419960 | AcroQoL | EQ-5D | Endochrine disorder | 0.563 | 0.183 | 17.36% | 52.22% | Useful/Caution (Amber) |
| 2018 | Woodcock, F., Doble, B., & CANCER 2015 Consortium (2018). Mapping the EORTC-QLQ-C30 to the EQ-5D-3L: An Assessment of Existing and Newly Developed Algorithms. Medical decision making : an international journal of the Society for Medical Decision Making, 38(8), 954–967. https://doi.org/10.1177/0272989X18797588 | EORTC QLQ-C30 | EQ-5D | Oncology | 0.670 | 0.068 | 62.37% | 69.75% | Ready to Use (Green) |
| 2018 | Wee, H. L., Yeo, K. K., Chong, K. J., Khoo, E. Y. H., & Cheung, Y. B. (2018). Mean Rank, Equipercentile, and Regression Mapping of World Health Organization Quality of Life Brief (WHOQOL-BREF) to EuroQoL 5 Dimensions 5 Levels (EQ-5D-5L) Utilities. Medical decision making : an international journal of the Society for Medical Decision Making, 38(3), 319–333. https://doi.org/10.1177/0272989X18756890 | WHOQOL-BREF | EQ-5D-5L | Mixed Disease Types | 0.451 | 0.013 | 79.45% | 32.29% | Useful/Caution (Amber) |
| 2018 | Peak, J., Goranitis, I., Day, E., Copello, A., Freemantle, N., & Frew, E. (2018). Predicting health-related quality of life (EQ-5D-5 L) and capability wellbeing (ICECAP-A) in the context of opiate dependence using routine clinical outcome measures: CORE-OM, LDQ and TOP. Health and quality of life outcomes, 16(1), 106. https://doi.org/10.1186/s12955-018-0926-9 | LDQ | EQ-5D-5L | Mental Health | 0.250 | 0.178 | 18.57% | 9.36% | Poor (Red) |
| 2018 | Peak, J., Goranitis, I., Day, E., Copello, A., Freemantle, N., & Frew, E. (2018). Predicting health-related quality of life (EQ-5D-5 L) and capability wellbeing (ICECAP-A) in the context of opiate dependence using routine clinical outcome measures: CORE-OM, LDQ and TOP. Health and quality of life outcomes, 16(1), 106. https://doi.org/10.1186/s12955-018-0926-7 | CORE-OM | EQ-5D-5L | Mental Health | 0.355 | 0.134 | 34.52% | 19.03% | Poor (Red) |
| 2018 | Peak, J., Goranitis, I., Day, E., Copello, A., Freemantle, N., & Frew, E. (2018). Predicting health-related quality of life (EQ-5D-5 L) and capability wellbeing (ICECAP-A) in the context of opiate dependence using routine clinical outcome measures: CORE-OM, LDQ and TOP. Health and quality of life outcomes, 16(1), 106. https://doi.org/10.1186/s12955-018-0926-12 | TOP | ICECAP-A | Mental Health | 0.335 | 0.151 | 27.96% | 16.72% | Poor (Red) |
| 2018 | Peak, J., Goranitis, I., Day, E., Copello, A., Freemantle, N., & Frew, E. (2018). Predicting health-related quality of life (EQ-5D-5 L) and capability wellbeing (ICECAP-A) in the context of opiate dependence using routine clinical outcome measures: CORE-OM, LDQ and TOP. Health and quality of life outcomes, 16(1), 106. https://doi.org/10.1186/s12955-018-0926-11 | TOP | EQ-5D-5L | Mental Health | 0.298 | 0.167 | 22.47% | 13.37% | Poor (Red) |
| 2018 | Peak, J., Goranitis, I., Day, E., Copello, A., Freemantle, N., & Frew, E. (2018). Predicting health-related quality of life (EQ-5D-5 L) and capability wellbeing (ICECAP-A) in the context of opiate dependence using routine clinical outcome measures: CORE-OM, LDQ and TOP. Health and quality of life outcomes, 16(1), 106. https://doi.org/10.1186/s12955-018-0926-10 | LDQ | ICECAP-A | Mental Health | 0.214 | 0.128 | 37.31% | 7.03% | Poor (Red) |
| 2018 | Lamu, A. N., Chen, G., Gamst-Klaussen, T., & Olsen, J. A. (2018). Do country-specific preference weights matter in the choice of mapping algorithms? The case of mapping the Diabetes-39 onto eight country-specific EQ-5D-5L value sets. Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation, 27(7), 1801–1814. https://doi.org/10.1007/s11136-018-1840-5 | D-39 | EQ-5D-5L | Chronic Disease | 0.546 | 0.101 | 48.48% | 48.89% | Poor (Red) |
| 2018 | Lamu, A. N., & Olsen, J. A. (2018). Testing alternative regression models to predict utilities: mapping the QLQ-C30 onto the EQ-5D-5L and the SF-6D. Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation, 27(11), 2823–2839. https://doi.org/10.1007/s11136-018-1981-7 | EORTC QLQ-C30 | SF-6D | Oncology | 0.727 | 0.070 | 62.01% | 76.15% | Ready to Use (Green) |
| 2018 | Lamu, A. N., & Olsen, J. A. (2018). Testing alternative regression models to predict utilities: mapping the QLQ-C30 onto the EQ-5D-5L and the SF-6D. Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation, 27(11), 2823–2839. https://doi.org/10.1007/s11136-018-1981-6 | EORTC QLQ-C30 | EQ-5D-5L | Oncology | 0.680 | 0.119 | 40.95% | 70.72% | Useful/Caution (Amber) |
| 2018 | Gamst-Klaussen, T., Lamu, A. N., Chen, G., & Olsen, J. A. (2018). Assessment of outcome measures for cost-utility analysis in depression: mapping depression scales onto the EQ-5D-5L. BJPsych open, 4(4), 160–166. https://doi.org/10.1192/bjo.2018.22 | PI-NRS-11 | EQ-5D-5L | Mental Health | 0.335 | 0.154 | 27.44% | 16.55% | Poor (Red) |
| 2018 | Gamst-Klaussen, T., Lamu, A. N., Chen, G., & Olsen, J. A. (2018). Assessment of outcome measures for cost-utility analysis in depression: mapping depression scales onto the EQ-5D-5L. BJPsych open, 4(4), 160–166. https://doi.org/10.1192/bjo.2018.21 | DASS-21 | EQ-5D-5L | Mental Health | 0.339 | 0.153 | 27.74% | 16.78% | Poor (Red) |
| 2018 | Chen, G., Garcia-Gordillo, M. A., Collado-Mateo, D., Del Pozo-Cruz, B., Adsuar, J. C., Cordero-Ferrera, J. M., Abellán-Perpiñán, J. M., & Sánchez-Martínez, F. I. (2018). Converting Parkinson-Specific Scores into Health State Utilities to Assess Cost-Utility Analysis. The patient, 11(6), 665–675. https://doi.org/10.1007/s40271-018-0317-5 | PDQ-8 | EQ-5D-5L | Neurology | 0.585 | 0.138 | 33.44% | 54.96% | Useful/Caution (Amber) |
| 2018 | Chen, G., Garcia-Gordillo, M. A., Collado-Mateo, D., Del Pozo-Cruz, B., Adsuar, J. C., Cordero-Ferrera, J. M., Abellán-Perpiñán, J. M., & Sánchez-Martínez, F. I. (2018). Converting Parkinson-Specific Scores into Health State Utilities to Assess Cost-Utility Analysis. The patient, 11(6), 665–675. https://doi.org/10.1007/s40271-018-0317-5 | PDQ-8 | EQ-5D-3L | Neurology | 0.619 | 0.192 | 14.27% | 62.08% | Useful/Caution (Amber) |
| 2018 | Chen, G., Garcia-Gordillo, M. A., Collado-Mateo, D., Del Pozo-Cruz, B., Adsuar, J. C., Cordero-Ferrera, J. M., Abellán-Perpiñán, J. M., & Sánchez-Martínez, F. I. (2018). Converting Parkinson-Specific Scores into Health State Utilities to Assess Cost-Utility Analysis. The patient, 11(6), 665–675. https://doi.org/10.1007/s40271-018-0317-5 | PDQ-8 | 15D | Neurology | 0.600 | 0.088 | 54.69% | 58.07% | Ready to Use (Green) |
| 2018 | C. Coon, A. Bushmakin, S. Tatlock, N. Williamson, M. Moffatt, R. Arbuckle & L. Abraham (2018) Evaluation of a crosswalk between the European Quality of Life Five Dimension Five Level and the Menopause-Specific Quality of Life questionnaire, Climacteric, 21:6, 566-573, DOI: 10.1080/13697137.2018.1481381 | Menopause-Specific Quality of Life questionnaire (MENQOL) | EQ-5D-5L | Urogenital | 0.347 | 0.093 | 52.83% | 17.84% | Poor (Red) |
| 2018 | Abdin, E., Chong, S. A., Seow, E., Verma, S., Tan, K. B., Subramaniam, M. (2018). Mapping the Positive and Negative Syndrome Scale scores to EQ-5D-5L and SF-6D utility scores in patients with schizophrenia. Qual Life Res, 28 (1), 177-186. | Positive And Negative Syndrome Scale (PANSS) | SF-6D | Mental Health | 0.272 | 0.016 | 79.24% | 10.84% | Useful/Caution (Amber) |
| 2018 | Abdin, E., Chong, S. A., Seow, E., Verma, S., Tan, K. B., Subramaniam, M. (2018). Mapping the Positive and Negative Syndrome Scale scores to EQ-5D-5L and SF-6D utility scores in patients with schizophrenia. Qual Life Res, 28 (1), 177-186. | Positive And Negative Syndrome Scale (PANSS) | EQ-5D-5L | Mental Health | 0.338 | 0.033 | 74.55% | 17.34% | Useful/Caution (Amber) |
| 2017 | Wong, C. K. H., Cheung, P. W. H., Samartzis, D., Luk, K. D., Cheung, K. M. C., Lam, C. L. K., & Cheung, J. P. Y. (2017). Mapping the SRS-22r questionnaire onto the EQ-5D-5L utility score in patients with adolescent idiopathic scoliosis. PloS one, 12(4), e0175847. https://doi.org/10.1371/journal.pone.0175847 | SRS-22r | EQ-5D-5L | Musculoskeletal | 0.609 | 0.071 | 61.49% | 59.81% | Ready to Use (Green) |
| 2017 | Thompson, N. R., Lapin, B. R., & Katzan, I. L. (2017). Mapping PROMIS Global Health Items to EuroQol (EQ-5D) Utility Scores Using Linear and Equipercentile Equating. PharmacoEconomics, 35(11), 1167–1176. https://doi.org/10.1007/s40273-017-0541-1 | PROMIS-GH | EQ-5D | Mixed Disease Types | 0.664 | 0.010 | 80.55% | 67.34% | Ready to Use (Green) |
| 2017 | Mlcoch, T., Tuzil, J., Sedova, L., Stolfa, J., Urbanova, M., Suchy, D., Smrzova, A., Jircikova, J., Hrnciarova, T., Pavelka, K., & Dolezal, T. (2018). Mapping Quality of Life (EQ-5D) from DAPsA, Clinical DAPsA and HAQ in Psoriatic Arthritis. The patient, 11(3), 329–340. https://doi.org/10.1007/s40271-017-0285-1 | cDAPsA + HAQ + activity impairment, disability, affected body surface area | EQ-5D | Musculoskeletal | 0.641 | 0.024 | 76.18% | 64.52% | Ready to Use (Green) |
| 2017 | Mlcoch, T., Tuzil, J., Sedova, L., Stolfa, J., Urbanova, M., Suchy, D., Smrzova, A., Jircikova, J., Hrnciarova, T., Pavelka, K., & Dolezal, T. (2018). Mapping Quality of Life (EQ-5D) from DAPsA, Clinical DAPsA and HAQ in Psoriatic Arthritis. The patient, 11(3), 329–340. https://doi.org/10.1007/s40271-017-0285-1 | cDAPsA + HAQ | EQ-5D | Musculoskeletal | 0.828 | 0.028 | 74.75% | 86.96% | Ready to Use (Green) |
| 2017 | Mlcoch, T., Tuzil, J., Sedova, L., Stolfa, J., Urbanova, M., Suchy, D., Smrzova, A., Jircikova, J., Hrnciarova, T., Pavelka, K., & Dolezal, T. (2018). Mapping Quality of Life (EQ-5D) from DAPsA, Clinical DAPsA and HAQ in Psoriatic Arthritis. The patient, 11(3), 329–340. https://doi.org/10.1007/s40271-017-0285-1 | cDAPsA | EQ-5D | Musculoskeletal | 0.386 | 0.051 | 67.54% | 23.25% | Useful/Caution (Amber) |
| 2017 | Mlcoch, T., Tuzil, J., Sedova, L., Stolfa, J., Urbanova, M., Suchy, D., Smrzova, A., Jircikova, J., Hrnciarova, T., Pavelka, K., & Dolezal, T. (2018). Mapping Quality of Life (EQ-5D) from DAPsA, Clinical DAPsA and HAQ in Psoriatic Arthritis. The patient, 11(3), 329–340. https://doi.org/10.1007/s40271-017-0285-1 | HAQ | EQ-5D | Musculoskeletal | 0.796 | 0.024 | 77.17% | 85.20% | Ready to Use (Green) |
| 2017 | Mlcoch, T., Tuzil, J., Sedova, L., Stolfa, J., Urbanova, M., Suchy, D., Smrzova, A., Jircikova, J., Hrnciarova, T., Pavelka, K., & Dolezal, T. (2018). Mapping Quality of Life (EQ-5D) from DAPsA, Clinical DAPsA and HAQ in Psoriatic Arthritis. The patient, 11(3), 329–340. https://doi.org/10.1007/s40271-017-0285-1 | DAPsA + HAQ + activity impairment, disability, affected body surface area | EQ-5D | Musculoskeletal | 0.643 | 0.024 | 76.32% | 64.22% | Ready to Use (Green) |
| 2017 | Mlcoch, T., Tuzil, J., Sedova, L., Stolfa, J., Urbanova, M., Suchy, D., Smrzova, A., Jircikova, J., Hrnciarova, T., Pavelka, K., & Dolezal, T. (2018). Mapping Quality of Life (EQ-5D) from DAPsA, Clinical DAPsA and HAQ in Psoriatic Arthritis. The patient, 11(3), 329–340. https://doi.org/10.1007/s40271-017-0285-1 | DAPsA + HAQ | EQ-5D | Musculoskeletal | 0.825 | 0.028 | 75.36% | 87.90% | Ready to Use (Green) |
| 2017 | Mlcoch, T., Tuzil, J., Sedova, L., Stolfa, J., Urbanova, M., Suchy, D., Smrzova, A., Jircikova, J., Hrnciarova, T., Pavelka, K., & Dolezal, T. (2018). Mapping Quality of Life (EQ-5D) from DAPsA, Clinical DAPsA and HAQ in Psoriatic Arthritis. The patient, 11(3), 329–340. https://doi.org/10.1007/s40271-017-0285-1 | DAPsA | EQ-5D | Musculoskeletal | 0.400 | 0.050 | 68.86% | 25.65% | Useful/Caution (Amber) |
| 2017 | Franklin, M., Payne, K., & Elliott, R. A. (2018). Quantifying the Relationship between Capability and Health in Older People: Can't Map, Won't Map. Medical decision making : an international journal of the Society for Medical Decision Making, 38(1), 79–94. https://doi.org/10.1177/0272989X17732975 | ICECAP-O | EQ-5D-3L | Mixed Disease Types | 0.353 | 0.163 | 23.81% | 18.23% | Poor (Red) |
| 2017 | Crump, R. T., Lai, E., Liu, G., Janjua, A., & Sutherland, J. M. (2017). Establishing utility values for the 22-item Sino-Nasal Outcome Test (SNOT-22) using a crosswalk to the EuroQol-five-dimensional questionnaire-three-level version (EQ-5D-3L). International forum of allergy & rhinology, 7(5), 480–487. https://doi.org/10.1002/alr.21917 | SNOT-22 | EQ-5D | Respiratory | 0.337 | 0.231 | 6.06% | 16.46% | Poor (Red) |
| 2017 | Crott R. (2018). Direct Mapping of the QLQ-C30 to EQ-5D Preferences: A Comparison of Regression Methods. PharmacoEconomics - open, 2(2), 165–177. https://doi.org/10.1007/s41669-017-0049-9 | QLQ-C30 | EQ-5D | Oncology | 0.580 | 0.104 | 47.40% | 54.77% | Useful/Caution (Amber) |
| 2017 | Collado-Mateo, D., Chen, G., Garcia-Gordillo, M. A., Iezzi, A., Adsuar, J. C., Olivares, P. R., & Gusi, N. (2017). "Fibromyalgia and quality of life: mapping the revised fibromyalgia impact questionnaire to the preference-based instruments". Health and quality of life outcomes, 15(1), 114. https://doi.org/10.1186/s12955-017-0690-3 | FIQR | SF-6D | CNS | 0.468 | 0.079 | 59.08% | 35.32% | Useful/Caution (Amber) |
| 2017 | Collado-Mateo, D., Chen, G., Garcia-Gordillo, M. A., Iezzi, A., Adsuar, J. C., Olivares, P. R., & Gusi, N. (2017). "Fibromyalgia and quality of life: mapping the revised fibromyalgia impact questionnaire to the preference-based instruments". Health and quality of life outcomes, 15(1), 114. https://doi.org/10.1186/s12955-017-0690-2 | FIQR | AQoL-8D | CNS | 0.465 | 0.123 | 40.12% | 34.45% | Poor (Red) |
| 2017 | Collado-Mateo, D., Chen, G., Garcia-Gordillo, M. A., Iezzi, A., Adsuar, J. C., Olivares, P. R., & Gusi, N. (2017). "Fibromyalgia and quality of life: mapping the revised fibromyalgia impact questionnaire to the preference-based instruments". Health and quality of life outcomes, 15(1), 114. https://doi.org/10.1186/s12955-017-0690-1 | FIQR | 15D | CNS | 0.507 | 0.087 | 54.79% | 41.97% | Useful/Caution (Amber) |
| 2017 | Collado-Mateo, D., Chen, G., Garcia-Gordillo, M. A., Iezzi, A., Adsuar, J. C., Olivares, P. R., & Gusi, N. (2017). "Fibromyalgia and quality of life: mapping the revised fibromyalgia impact questionnaire to the preference-based instruments". Health and quality of life outcomes, 15(1), 114. https://doi.org/10.1186/s12955-017-0690-0 | FIQR | EQ-5D-5L | CNS | 0.579 | 0.174 | 19.66% | 54.56% | Useful/Caution (Amber) |
| 2016 | Ruiz, M. A., Gutiérrez, L. L., Monroy, M., & Rejas, J. (2016). Mapping of the OAB-SF Questionnaire onto EQ-5D in Spanish Patients with Overactive Bladder. Clinical drug investigation, 36(4), 267–279. https://doi.org/10.1007/s40261-016-0377-z | OAB-5D | EQ-5D | Urogenital | 0.892 | 0.197 | 12.90% | 91.98% | Useful/Caution (Amber) |
| 2016 | Kim, H. L., Kim, D., Jang, E. J., Lee, M. Y., Song, H. J., Park, S. Y., Cho, S. K., Sung, Y. K., Choi, C. B., Won, S., Bang, S. Y., Cha, H. S., Choe, J. Y., Chung, W. T., Hong, S. J., Jun, J. B., Kim, J., Kim, S. K., Kim, T. H., Kim, T. J., … Lee, E. K. (2016). Mapping health assessment questionnaire disability index (HAQ-DI) score, pain visual analog scale (VAS), and disease activity score in 28 joints (DAS28) onto the EuroQol-5D (EQ-5D) utility score with the KORean Observational study Network for Arthritis (KORONA) registry data. Rheumatology international, 36(4), 505–513. https://doi.org/10.1007/s00296-016-3427-4 | HAQ-DI & DAS28 & VAS | EQ-5D | Rheumatology | 0.576 | 0.164 | 22.77% | 53.91% | Useful/Caution (Amber) |
| 2016 | Kim, H. L., Kim, D., Jang, E. J., Lee, M. Y., Song, H. J., Park, S. Y., Cho, S. K., Sung, Y. K., Choi, C. B., Won, S., Bang, S. Y., Cha, H. S., Choe, J. Y., Chung, W. T., Hong, S. J., Jun, J. B., Kim, J., Kim, S. K., Kim, T. H., Kim, T. J., … Lee, E. K. (2016). Mapping health assessment questionnaire disability index (HAQ-DI) score, pain visual analog scale (VAS), and disease activity score in 28 joints (DAS28) onto the EuroQol-5D (EQ-5D) utility score with the KORean Observational study Network for Arthritis (KORONA) registry data. Rheumatology international, 36(4), 505–513. https://doi.org/10.1007/s00296-016-3427-3 | HAQ-DI & VAS | EQ-5D | Rheumatology | 0.570 | 0.167 | 22.47% | 52.79% | Useful/Caution (Amber) |
| 2016 | Kim, H. L., Kim, D., Jang, E. J., Lee, M. Y., Song, H. J., Park, S. Y., Cho, S. K., Sung, Y. K., Choi, C. B., Won, S., Bang, S. Y., Cha, H. S., Choe, J. Y., Chung, W. T., Hong, S. J., Jun, J. B., Kim, J., Kim, S. K., Kim, T. H., Kim, T. J., … Lee, E. K. (2016). Mapping health assessment questionnaire disability index (HAQ-DI) score, pain visual analog scale (VAS), and disease activity score in 28 joints (DAS28) onto the EuroQol-5D (EQ-5D) utility score with the KORean Observational study Network for Arthritis (KORONA) registry data. Rheumatology international, 36(4), 505–513. https://doi.org/10.1007/s00296-016-3427-2 | HAQ-DI & DAS28 | EQ-5D | Rheumatology | 0.539 | 0.170 | 21.36% | 48.01% | Poor (Red) |
| 2016 | Kim, H. L., Kim, D., Jang, E. J., Lee, M. Y., Song, H. J., Park, S. Y., Cho, S. K., Sung, Y. K., Choi, C. B., Won, S., Bang, S. Y., Cha, H. S., Choe, J. Y., Chung, W. T., Hong, S. J., Jun, J. B., Kim, J., Kim, S. K., Kim, T. H., Kim, T. J., … Lee, E. K. (2016). Mapping health assessment questionnaire disability index (HAQ-DI) score, pain visual analog scale (VAS), and disease activity score in 28 joints (DAS28) onto the EuroQol-5D (EQ-5D) utility score with the KORean Observational study Network for Arthritis (KORONA) registry data. Rheumatology international, 36(4), 505–513. https://doi.org/10.1007/s00296-016-3427-1 | HAQ-DI | EQ-5D | Rheumatology | 0.515 | 0.173 | 19.73% | 43.31% | Poor (Red) |
| 2016 | Khan, I., Morris, S., Pashayan, N., Matata, B., Bashir, Z., & Maguirre, J. (2016). Comparing the mapping between EQ-5D-5L, EQ-5D-3L and the EORTC-QLQ-C30 in non-small cell lung cancer patients. Health and quality of life outcomes, 14, 60. https://doi.org/10.1186/s12955-016-0455-2 | EORTC QLQ-C30 | EQ-5D-3L | Oncology | 0.690 | 0.113 | 44.43% | 72.93% | Useful/Caution (Amber) |
| 2016 | Khan, I., Morris, S., Pashayan, N., Matata, B., Bashir, Z., & Maguirre, J. (2016). Comparing the mapping between EQ-5D-5L, EQ-5D-3L and the EORTC-QLQ-C30 in non-small cell lung cancer patients. Health and quality of life outcomes, 14, 60. https://doi.org/10.1186/s12955-016-0455-1 | EORTC QLQ-C30 | EQ-5D-5L | Oncology | 0.750 | 0.092 | 51.97% | 80.62% | Useful/Caution (Amber) |
| 2016 | Hoyle, C. K., Tabberer, M., & Brooks, J. (2016). Mapping the COPD Assessment Test onto EQ-5D. Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research, 19(4), 469–477. https://doi.org/10.1016/j.jval.2016.01.005 | COPD Assessment test | EQ-5D | Respiratory | 0.377 | 0.162 | 23.66% | 20.89% | Poor (Red) |
| 2016 | Hatswell, A. J., & Vegter, S. (2016). Measuring quality of life in opioid-induced constipation: mapping EQ-5D-3 L and PAC-QOL. Health economics review, 6(1), 14. https://doi.org/10.1186/s13561-016-0091-9 | PAC-QOL | EQ-5D | Stomach & Bowel | 0.068 | 0.314 | 0.48% | 1.82% | Poor (Red) |
| 2016 | Emily-Ruth Marriott, Guy van Hazel, Peter Gibbs & Anthony J. Hatswell (2017) Mapping EORTC-QLQ-C30 to EQ-5D-3L in patients with colorectal cancer, Journal of Medical Economics, 20:2, 193-199, DOI: 10.1080/13696998.2016.1241788 | EORTC QLQ-C30 | EQ-5D-3L | Oncology | 0.646 | 0.092 | 52.84% | 65.38% | Useful/Caution (Amber) |
| 2016 | Browne, C., Brazier, J., Carlton, J., Alavi, Y., & Jofre-Bonet, M. (2012). Estimating quality-adjusted life years from patient-reported visual functioning. Eye (London, England), 26(10), 1295–1301. https://doi.org/10.1038/eye.2012.137 | VFQ-25 | EQ-5D | Ophthalmology | 0.335 | 0.034 | 73.90% | 17.11% | Useful/Caution (Amber) |
| 2015 | Kharroubi, S. A., Edlin, R., Meads, D., Browne, C., Brown, J., & McCabe, C. (2015). Use of Bayesian Markov chain Monte Carlo methods to estimate EQ-5D utility scores from EORTC QLQ data in myeloma for use in cost-effectiveness analysis. Medical decision making : an international journal of the Society for Medical Decision Making, 35(3), 351–360. https://doi.org/10.1177/0272989X15575285 | EORTC QLQ-C30 & QLQ-MY20 Myeloma module | EQ-5D | Oncology | 0.700 | 0.186 | 16.88% | 74.29% | Useful/Caution (Amber) |
| 2015 | Chen, G., Iezzi, A., McKie, J., Khan, M. A., & Richardson, J. (2015). Diabetes and quality of life: Comparing results from utility instruments and Diabetes-39. Diabetes research and clinical practice, 109(2), 326–333. https://doi.org/10.1016/j.diabres.2015.05.016 | Diabetes-39 | AQoL-8D | Chronic Disease | 0.610 | 0.140 | 32.03% | 60.11% | Useful/Caution (Amber) |
| 2015 | Chen, G., Iezzi, A., McKie, J., Khan, M. A., & Richardson, J. (2015). Diabetes and quality of life: Comparing results from utility instruments and Diabetes-39. Diabetes research and clinical practice, 109(2), 326–333. https://doi.org/10.1016/j.diabres.2015.05.015 | Diabetes-39 | 15D | Chronic Disease | 0.587 | 0.083 | 57.33% | 55.90% | Ready to Use (Green) |
| 2015 | Chen, G., Iezzi, A., McKie, J., Khan, M. A., & Richardson, J. (2015). Diabetes and quality of life: Comparing results from utility instruments and Diabetes-39. Diabetes research and clinical practice, 109(2), 326–333. https://doi.org/10.1016/j.diabres.2015.05.014 | Diabetes-39 | QWB | Chronic Disease | 0.428 | 0.113 | 43.08% | 28.36% | Poor (Red) |
| 2015 | Chen, G., Iezzi, A., McKie, J., Khan, M. A., & Richardson, J. (2015). Diabetes and quality of life: Comparing results from utility instruments and Diabetes-39. Diabetes research and clinical practice, 109(2), 326–333. https://doi.org/10.1016/j.diabres.2015.05.013 | Diabetes-39 | HUI3 | Chronic Disease | 0.490 | 0.198 | 12.09% | 38.33% | Poor (Red) |
| 2015 | Chen, G., Iezzi, A., McKie, J., Khan, M. A., & Richardson, J. (2015). Diabetes and quality of life: Comparing results from utility instruments and Diabetes-39. Diabetes research and clinical practice, 109(2), 326–333. https://doi.org/10.1016/j.diabres.2015.05.012 | Diabetes-39 | SF-6D | Chronic Disease | 0.599 | 0.089 | 54.26% | 57.92% | Ready to Use (Green) |
| 2015 | Chen, G., Iezzi, A., McKie, J., Khan, M. A., & Richardson, J. (2015). Diabetes and quality of life: Comparing results from utility instruments and Diabetes-39. Diabetes research and clinical practice, 109(2), 326–333. https://doi.org/10.1016/j.diabres.2015.05.011 | Diabetes-39 | EQ-5D | Chronic Disease | 0.475 | 0.177 | 19.24% | 36.09% | Poor (Red) |
| 2014 | Vokó, Z., Németh, R., Nagyjánosi, L., Jermendy, G., Winkler, G., Hídvégi, T., Kalotai, Z., & Kaló, Z. (2014). Mapping the Nottingham Health Profile onto the Preference-Based EuroQol-5D Instrument for Patients with Diabetes. Value in health regional issues, 4, 31–36. https://doi.org/10.1016/j.vhri.2014.06.002 | Nottingham health profile | EQ-5D | Chronic Disease | 0.680 | 0.174 | 19.47% | 71.51% | Useful/Caution (Amber) |
| 2014 | Skaltsa, K., Longworth, L., Ivanescu, C., Phung, D., & Holmstrom, S. (2014). Mapping the FACT-P to the preference-based EQ-5D questionnaire in metastatic castration-resistant prostate cancer. Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research, 17(2), 238–244. https://doi.org/10.1016/j.jval.2013.12.005 | FACT-P | EQ-5D | Oncology | 0.718 | 0.162 | 23.87% | 76.02% | Useful/Caution (Amber) |
| 2014 | Rundell, S. D., Bresnahan, B. W., Heagerty, P. J., Comstock, B. A., Friedly, J. L., Jarvik, J. G., & Sullivan, S. D. (2014). Mapping a patient-reported functional outcome measure to a utility measure for comparative effectiveness and economic evaluations in older adults with low back pain. Medical decision making : an international journal of the Society for Medical Decision Making, 34(7), 873–883. https://doi.org/10.1177/0272989X14533996 | RMDQ and numerical rating scales of pain | EQ-5D | Musculoskeletal | 0.571 | 0.102 | 48.86% | 53.12% | Useful/Caution (Amber) |
| 2014 | Rundell, S. D., Bresnahan, B. W., Heagerty, P. J., Comstock, B. A., Friedly, J. L., Jarvik, J. G., & Sullivan, S. D. (2014). Mapping a patient-reported functional outcome measure to a utility measure for comparative effectiveness and economic evaluations in older adults with low back pain. Medical decision making : an international journal of the Society for Medical Decision Making, 34(7), 873–883. https://doi.org/10.1177/0272989X14533995 | RMDQ | EQ-5D | Musculoskeletal | 0.503 | 0.110 | 45.97% | 41.03% | Poor (Red) |
| 2014 | Proskorovsky, I., Lewis, P., Williams, C. D., Jordan, K., Kyriakou, C., Ishak, J., & Davies, F. E. (2014). Mapping EORTC QLQ-C30 and QLQ-MY20 to EQ-5D in patients with multiple myeloma. Health and quality of life outcomes, 12, 35. https://doi.org/10.1186/1477-7525-12-36 | EORTC QLQ-C30 | EQ-5D | Oncology | 0.696 | 0.165 | 23.10% | 73.00% | Useful/Caution (Amber) |
| 2014 | Proskorovsky, I., Lewis, P., Williams, C. D., Jordan, K., Kyriakou, C., Ishak, J., & Davies, F. E. (2014). Mapping EORTC QLQ-C30 and QLQ-MY20 to EQ-5D in patients with multiple myeloma. Health and quality of life outcomes, 12, 35. https://doi.org/10.1186/1477-7525-12-35 | EORTC QLQ-C30 & QLQ-MY20 | EQ-5D | Oncology | 0.703 | 0.163 | 23.53% | 74.29% | Useful/Caution (Amber) |
| 2014 | Le Q. A. (2014). Probabilistic mapping of the health status measure SF-12 onto the health utility measure EQ-5D using the US-population-based scoring models. Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation, 23(2), 459–466. https://doi.org/10.1007/s11136-013-0517-3 | SF-12 | EQ-5D | Mixed Disease Types | 0.773 | 0.007 | 81.62% | 82.85% | Ready to Use (Green) |
| 2014 | Kim, S. H., Kim, S. O., Lee, S. I., & Jo, M. W. (2014). Deriving a mapping algorithm for converting SF-36 scores to EQ-5D utility score in a Korean population. Health and quality of life outcomes, 12, 145. https://doi.org/10.1186/s12955-014-0145-9 | SF-36 | EQ-5D | Mixed Disease Types | 0.681 | 0.114 | 43.58% | 71.10% | Useful/Caution (Amber) |
| 2014 | Khan, K. A., Petrou, S., Rivero-Arias, O., Walters, S. J., & Boyle, S. E. (2014). Mapping EQ-5D utility scores from the PedsQL™ generic core scales. PharmacoEconomics, 32(7), 693–706. https://doi.org/10.1007/s40273-014-0153-y | PedsQL General core scales | EQ-5D | Mixed Disease Types | 0.287 | 0.023 | 76.38% | 12.31% | Useful/Caution (Amber) |
| 2014 | Khan, I., & Morris, S. (2014). A non-linear beta-binomial regression model for mapping EORTC QLQ- C30 to the EQ-5D-3L in lung cancer patients: a comparison with existing approaches. Health and quality of life outcomes, 12, 163. https://doi.org/10.1186/s12955-014-0163-7 | EORTC QLQ-C30 | EQ-5D-3L | Oncology | 0.750 | 0.090 | 53.86% | 80.20% | Ready to Use (Green) |
| 2014 | Diels, J., Hamberg, P., Ford, D., Price, P. W., Spencer, M., & Dass, R. N. (2015). Mapping FACT-P to EQ-5D in a large cross-sectional study of metastatic castration-resistant prostate cancer patients. Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation, 24(3), 591–598. https://doi.org/10.1007/s11136-014-0794-5 | FACT-P | EQ-5D | Oncology | 0.612 | 0.198 | 13.12% | 60.17% | Useful/Caution (Amber) |
| 2014 | Cheung, Y. B., Luo, N., Ng, R., & Lee, C. F. (2014). Mapping the functional assessment of cancer therapy-breast (FACT-B) to the 5-level EuroQoL Group's 5-dimension questionnaire (EQ-5D-5L) utility index in a multi-ethnic Asian population. Health and quality of life outcomes, 12, 180. https://doi.org/10.1186/s12955-014-0180-6 | FACT-B | EQ-5D-5L | Oncology | 0.489 | 0.013 | 79.04% | 39.11% | Useful/Caution (Amber) |
| 2014 | Chan, K. K., Willan, A. R., Gupta, M., & Pullenayegum, E. (2014). Underestimation of uncertainties in health utilities derived from mapping algorithms involving health-related quality-of-life measures: statistical explanations and potential remedies. Medical decision making : an international journal of the Society for Medical Decision Making, 34(7), 863–872. https://doi.org/10.1177/0272989X13517750 | UW QOL v4 | EQ-5D | Oncology | 0.597 | 0.116 | 41.73% | 57.71% | Useful/Caution (Amber) |
| 2014 | Brazier, J., Connell, J., Papaioannou, D., Mukuria, C., Mulhern, B., Peasgood, T., Jones, M. L., Paisley, S., O'Cathain, A., Barkham, M., Knapp, M., Byford, S., Gilbody, S., & Parry, G. (2014). A systematic review, psychometric analysis and qualitative assessment of generic preference-based measures of health in mental health populations and the estimation of mapping functions from widely used specific measures. Health technology assessment (Winchester, England), 18(34), vii–188. https://doi.org/10.3310/hta18347 | GHQ-12 | SF-6D | Mental Health | 0.047 | 0.077 | 59.11% | 1.51% | Useful/Caution (Amber) |
| 2014 | Brazier, J., Connell, J., Papaioannou, D., Mukuria, C., Mulhern, B., Peasgood, T., Jones, M. L., Paisley, S., O'Cathain, A., Barkham, M., Knapp, M., Byford, S., Gilbody, S., & Parry, G. (2014). A systematic review, psychometric analysis and qualitative assessment of generic preference-based measures of health in mental health populations and the estimation of mapping functions from widely used specific measures. Health technology assessment (Winchester, England), 18(34), vii–188. https://doi.org/10.3310/hta18346 | CORE-OM | SF-6D | Mental Health | 0.576 | 0.054 | 67.17% | 53.83% | Ready to Use (Green) |
| 2014 | Brazier, J., Connell, J., Papaioannou, D., Mukuria, C., Mulhern, B., Peasgood, T., Jones, M. L., Paisley, S., O'Cathain, A., Barkham, M., Knapp, M., Byford, S., Gilbody, S., & Parry, G. (2014). A systematic review, psychometric analysis and qualitative assessment of generic preference-based measures of health in mental health populations and the estimation of mapping functions from widely used specific measures. Health technology assessment (Winchester, England), 18(34), vii–188. https://doi.org/10.3310/hta18345 | GAD-7 & PHQ-9 | SF-6D | Mental Health | 0.487 | 0.067 | 63.04% | 39.02% | Useful/Caution (Amber) |
| 2014 | Brazier, J., Connell, J., Papaioannou, D., Mukuria, C., Mulhern, B., Peasgood, T., Jones, M. L., Paisley, S., O'Cathain, A., Barkham, M., Knapp, M., Byford, S., Gilbody, S., & Parry, G. (2014). A systematic review, psychometric analysis and qualitative assessment of generic preference-based measures of health in mental health populations and the estimation of mapping functions from widely used specific measures. Health technology assessment (Winchester, England), 18(34), vii–188. https://doi.org/10.3310/hta18344 | GAD-7 | SF-6D | Mental Health | 0.342 | 0.079 | 57.81% | 17.39% | Useful/Caution (Amber) |
| 2014 | Brazier, J., Connell, J., Papaioannou, D., Mukuria, C., Mulhern, B., Peasgood, T., Jones, M. L., Paisley, S., O'Cathain, A., Barkham, M., Knapp, M., Byford, S., Gilbody, S., & Parry, G. (2014). A systematic review, psychometric analysis and qualitative assessment of generic preference-based measures of health in mental health populations and the estimation of mapping functions from widely used specific measures. Health technology assessment (Winchester, England), 18(34), vii–188. https://doi.org/10.3310/hta18343 | PHQ-9 | SF-6D | Mental Health | 0.476 | 0.070 | 61.27% | 37.12% | Useful/Caution (Amber) |
| 2014 | Brazier, J., Connell, J., Papaioannou, D., Mukuria, C., Mulhern, B., Peasgood, T., Jones, M. L., Paisley, S., O'Cathain, A., Barkham, M., Knapp, M., Byford, S., Gilbody, S., & Parry, G. (2014). A systematic review, psychometric analysis and qualitative assessment of generic preference-based measures of health in mental health populations and the estimation of mapping functions from widely used specific measures. Health technology assessment (Winchester, England), 18(34), vii–188. https://doi.org/10.3310/hta18342 | HADS | SF-6D | Mental Health | 0.319 | 0.057 | 65.55% | 15.42% | Useful/Caution (Amber) |
| 2014 | Brazier, J., Connell, J., Papaioannou, D., Mukuria, C., Mulhern, B., Peasgood, T., Jones, M. L., Paisley, S., O'Cathain, A., Barkham, M., Knapp, M., Byford, S., Gilbody, S., & Parry, G. (2014). A systematic review, psychometric analysis and qualitative assessment of generic preference-based measures of health in mental health populations and the estimation of mapping functions from widely used specific measures. Health technology assessment (Winchester, England), 18(34), vii–188. https://doi.org/10.3310/hta18341 | HADS | EQ-5D | Mental Health | 0.187 | 0.188 | 15.51% | 5.36% | Poor (Red) |
| 2014 | Brazier, J., Connell, J., Papaioannou, D., Mukuria, C., Mulhern, B., Peasgood, T., Jones, M. L., Paisley, S., O'Cathain, A., Barkham, M., Knapp, M., Byford, S., Gilbody, S., & Parry, G. (2014). A systematic review, psychometric analysis and qualitative assessment of generic preference-based measures of health in mental health populations and the estimation of mapping functions from widely used specific measures. Health technology assessment (Winchester, England), 18(34), vii–188. https://doi.org/10.3310/hta18340 | HADS | EQ-5D | Mental Health | 0.187 | 0.188 | 15.34% | 5.76% | Poor (Red) |
| 2013 | Young, M. K., Ng, S. K., Mellick, G., & Scuffham, P. A. (2013). Mapping of the PDQ-39 to EQ-5D scores in patients with Parkinson's disease. Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation, 22(5), 1065–1072. https://doi.org/10.1007/s11136-012-0231-6 | PDQ-39 | EQ-5D | Neurology | 0.557 | 0.152 | 27.71% | 49.95% | Poor (Red) |
| 2013 | Teckle, P., McTaggart-Cowan, H., Van der Hoek, K., Chia, S., Melosky, B., Gelmon, K., & Peacock, S. (2013). Mapping the FACT-G cancer-specific quality of life instrument to the EQ-5D and SF-6D. Health and quality of life outcomes, 11, 203. https://doi.org/10.1186/1477-7525-11-204 | FACT-G | SF-6D | Oncology | 0.651 | 0.061 | 64.76% | 66.64% | Ready to Use (Green) |
| 2013 | Teckle, P., McTaggart-Cowan, H., Van der Hoek, K., Chia, S., Melosky, B., Gelmon, K., & Peacock, S. (2013). Mapping the FACT-G cancer-specific quality of life instrument to the EQ-5D and SF-6D. Health and quality of life outcomes, 11, 203. https://doi.org/10.1186/1477-7525-11-203 | FACT-G | EQ-5D | Oncology | 0.469 | 0.095 | 51.83% | 35.14% | Poor (Red) |
| 2013 | Sidovar, M. F., Limone, B. L., Lee, S., & Coleman, C. I. (2013). Mapping the 12-item multiple sclerosis walking scale to the EuroQol 5-dimension index measure in North American multiple sclerosis patients. BMJ open, 3(5), e002798. https://doi.org/10.1136/bmjopen-2013-002798 | MSWS-12 | EQ-5D | CNS | 0.329 | 0.145 | 30.97% | 16.36% | Poor (Red) |
| 2013 | Kay, S., Tolley, K., Colayco, D., Khalaf, K., Anderson, P., & Globe, D. (2013). Mapping EQ-5D utility scores from the Incontinence Quality of Life Questionnaire among patients with neurogenic and idiopathic overactive bladder. Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research, 16(2), 394–402. https://doi.org/10.1016/j.jval.2012.12.005 | I-QOL | EQ-5D | Urogenital | 0.210 | 0.140 | 31.52% | 6.57% | Poor (Red) |
| 2013 | Ghatnekar, O., Eriksson, M., & Glader, E. L. (2013). Mapping health outcome measures from a stroke registry to EQ-5D weights. Health and quality of life outcomes, 11, 34. https://doi.org/10.1186/1477-7525-11-34 | Stroke outcome measures not restricted to validated instruments | EQ-5D | Cardiovascular | 0.724 | 0.056 | 65.60% | 78.34% | Ready to Use (Green) |
| 2013 | Dams, J., Klotsche, J., Bornschein, B., Reese, J. P., Balzer-Geldsetzer, M., Winter, Y., Schrag, A., Siderowf, A., Oertel, W. H., Deuschl, G., Siebert, U., & Dodel, R. (2013). Mapping the EQ-5D index by UPDRS and PDQ-8 in patients with Parkinson's disease. Health and quality of life outcomes, 11, 35. https://doi.org/10.1186/1477-7525-11-36 | PDQ-8 | EQ-5D | Neurology | 0.712 | 0.139 | 31.72% | 75.01% | Useful/Caution (Amber) |
| 2013 | Dams, J., Klotsche, J., Bornschein, B., Reese, J. P., Balzer-Geldsetzer, M., Winter, Y., Schrag, A., Siderowf, A., Oertel, W. H., Deuschl, G., Siebert, U., & Dodel, R. (2013). Mapping the EQ-5D index by UPDRS and PDQ-8 in patients with Parkinson's disease. Health and quality of life outcomes, 11, 35. https://doi.org/10.1186/1477-7525-11-35 | UPDRS | EQ-5D | Neurology | 0.712 | 0.139 | 32.21% | 74.56% | Useful/Caution (Amber) |
| 2013 | Badia, X., Roset, M., Valassi, E., Franz, H., Forsythe, A., & Webb, S. M. (2013). Mapping CushingQOL scores to EQ-5D utility values using data from the European Registry on Cushing's syndrome (ERCUSYN). Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation, 22(10), 2941–2950. https://doi.org/10.1007/s11136-013-0396-7 | CushingQol | EQ-5D | Endochrine disorder | 0.524 | 0.155 | 26.39% | 44.31% | Poor (Red) |
| 2012 | Versteegh, M. M., Leunis, A., Luime, J. J., Boggild, M., Uyl-de Groot, C. A., & Stolk, E. A. (2012). Mapping QLQ-C30, HAQ, and MSIS-29 on EQ-5D. Medical decision making : an international journal of the Society for Medical Decision Making, 32(4), 554–568. https://doi.org/10.1177/0272989X11427761 | QLQ-C30 | EQ-5D | Oncology | 0.740 | 0.130 | 36.31% | 79.32% | Useful/Caution (Amber) |
| 2012 | Versteegh, M. M., Leunis, A., Luime, J. J., Boggild, M., Uyl-de Groot, C. A., & Stolk, E. A. (2012). Mapping QLQ-C30, HAQ, and MSIS-29 on EQ-5D. Medical decision making : an international journal of the Society for Medical Decision Making, 32(4), 554–568. https://doi.org/10.1177/0272989X11427761 | MSIS-29 | EQ-5D | CNS | 0.580 | 0.190 | 14.77% | 54.45% | Useful/Caution (Amber) |
| 2012 | Versteegh, M. M., Leunis, A., Luime, J. J., Boggild, M., Uyl-de Groot, C. A., & Stolk, E. A. (2012). Mapping QLQ-C30, HAQ, and MSIS-29 on EQ-5D. Medical decision making : an international journal of the Society for Medical Decision Making, 32(4), 554–568. https://doi.org/10.1177/0272989X11427761 | HAQ | EQ-5D | Musculoskeletal | 0.540 | 0.500 | 47.86% | Poor (Red) | |
| 2012 | Versteegh, M. M., Leunis, A., Luime, J. J., Boggild, M., Uyl-de Groot, C. A., & Stolk, E. A. (2012). Mapping QLQ-C30, HAQ, and MSIS-29 on EQ-5D. Medical decision making : an international journal of the Society for Medical Decision Making, 32(4), 554–568. https://doi.org/10.1177/0272989X11427761 | HAQ | EQ-5D | Musculoskeletal | 0.390 | 0.150 | 28.72% | 22.62% | Poor (Red) |
| 2012 | Pinedo-Villanueva, R. A., Turner, D., Judge, A., Raftery, J. P., & Arden, N. K. (2013). Mapping the Oxford hip score onto the EQ-5D utility index. Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation, 22(3), 665–675. https://doi.org/10.1007/s11136-012-0174-y | OHS | EQ-5D | Musculoskeletal | 0.720 | 0.190 | 15.34% | 75.68% | Useful/Caution (Amber) |
| 2012 | Maund, E., Craig, D., Suekarran, S., Neilson, A., Wright, K., Brealey, S., Dennis, L., Goodchild, L., Hanchard, N., Rangan, A., Richardson, G., Robertson, J., & McDaid, C. (2012). Management of frozen shoulder: a systematic review and cost-effectiveness analysis. Health technology assessment (Winchester, England), 16(11), 1–264. https://doi.org/10.3310/hta16111 | SF-36 | EQ-5D | Musculoskeletal | 0.083 | 0.273 | 1.92% | 2.29% | Poor (Red) |
| 2012 | Maund, E., Craig, D., Suekarran, S., Neilson, A., Wright, K., Brealey, S., Dennis, L., Goodchild, L., Hanchard, N., Rangan, A., Richardson, G., Robertson, J., & McDaid, C. (2012). Management of frozen shoulder: a systematic review and cost-effectiveness analysis. Health technology assessment (Winchester, England), 16(11), 1–264. https://doi.org/10.3310/hta16110 | Visual analogue scale rating of pain | EQ-5D | Musculoskeletal | 0.008 | 0.277 | 2.29% | 1.08% | Poor (Red) |
| 2012 | Kontodimopoulos, N., Bozios, P., Yfantopoulos, J., & Niakas, D. (2013). Longitudinal predictive ability of mapping models: examining post-intervention EQ-5D utilities derived from baseline MHAQ data in rheumatoid arthritis patients. The European journal of health economics : HEPAC : health economics in prevention and care, 14(2), 307–314. https://doi.org/10.1007/s10198-012-0376-9 | MHAQ | EQ-5D | Rheumatology | 0.452 | 0.207 | 10.67% | 31.60% | Poor (Red) |
| 2012 | Kim, S. H., Jo, M. W., Kim, H. J., & Ahn, J. H. (2012). Mapping EORTC QLQ-C30 onto EQ-5D for the assessment of cancer patients. Health and quality of life outcomes, 10, 151. https://doi.org/10.1186/1477-7525-10-151 | EORTC QLQ-C30 | EQ-5D | Oncology | 0.516 | 0.095 | 51.79% | 44.17% | Poor (Red) |
| 2012 | Hawton, A., Green, C., Telford, C., Zajicek, J., & Wright, D. (2012). Using the Multiple Sclerosis Impact Scale to estimate health state utility values: mapping from the MSIS-29, version 2, to the EQ-5D and the SF-6D. Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research, 15(8), 1084–1091. https://doi.org/10.1016/j.jval.2012.07.008 | MSIS-29 | SF-6D | Neurology | 0.692 | 0.073 | 59.23% | 72.74% | Ready to Use (Green) |
| 2012 | Hawton, A., Green, C., Telford, C., Zajicek, J., & Wright, D. (2012). Using the Multiple Sclerosis Impact Scale to estimate health state utility values: mapping from the MSIS-29, version 2, to the EQ-5D and the SF-6D. Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research, 15(8), 1084–1091. https://doi.org/10.1016/j.jval.2012.07.007 | MSIS-29 | EQ-5D | Neurology | 0.506 | 0.202 | 12.48% | 41.06% | Poor (Red) |
| 2012 | Gu, N. Y., Bell, C., Botteman, M. F., Ji, X., Carter, J. A., & van Hout, B. (2012). Estimating preference-based EQ-5D health state utilities or item responses from neuropathic pain scores. The patient, 5(3), 185–197. https://doi.org/10.1007/BF03262491 | PI-NRS-11 | EQ-5D | Chronic Disease | 0.290 | 0.033 | 73.60% | 12.69% | Useful/Caution (Amber) |
| 2012 | Gillard, P. J., Devine, B., Varon, S. F., Liu, L., & Sullivan, S. D. (2012). Mapping from disease-specific measures to health-state utility values in individuals with migraine. Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research, 15(3), 485–494. https://doi.org/10.1016/j.jval.2011.12.008 | MSQ | EQ-5D | Neurology | 0.420 | 0.290 | 1.04% | 27.35% | Poor (Red) |
| 2012 | Gillard, P. J., Devine, B., Varon, S. F., Liu, L., & Sullivan, S. D. (2012). Mapping from disease-specific measures to health-state utility values in individuals with migraine. Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research, 15(3), 485–494. https://doi.org/10.1016/j.jval.2011.12.007 | HIT-6 | EQ-5D | Neurology | 0.340 | 0.300 | 0.87% | 16.90% | Poor (Red) |
| 2012 | Dakin, H., Gray, A., & Murray, D. (2013). Mapping analyses to estimate EQ-5D utilities and responses based on Oxford Knee Score. Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation, 22(3), 683–694. https://doi.org/10.1007/s11136-012-0189-4 | OKS | EQ-5D | Musculoskeletal | 0.690 | 0.035 | 73.25% | 71.84% | Ready to Use (Green) |
| 2012 | Crott, R., Versteegh, M., & Uyl-de-Groot, C. (2013). An assessment of the external validity of mapping QLQ-C30 to EQ-5D preferences. Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation, 22(5), 1045–1054. https://doi.org/10.1007/s11136-012-0220-9 | EORTC QLQ-C30 | EQ-5D | Oncology | 0.803 | 0.096 | 51.24% | 85.08% | Useful/Caution (Amber) |
| 2012 | Crott, R., Versteegh, M., & Uyl-de-Groot, C. (2013). An assessment of the external validity of mapping QLQ-C30 to EQ-5D preferences. Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation, 22(5), 1045–1054. https://doi.org/10.1007/s11136-012-0220-12 | EORTC QLQ-C30 | EQ-5D | Oncology | 0.588 | 0.183 | 16.52% | 55.22% | Useful/Caution (Amber) |
| 2012 | Crott, R., Versteegh, M., & Uyl-de-Groot, C. (2013). An assessment of the external validity of mapping QLQ-C30 to EQ-5D preferences. Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation, 22(5), 1045–1054. https://doi.org/10.1007/s11136-012-0220-11 | EORTC QLQ-C30 | EQ-5D | Oncology | 0.723 | 0.163 | 24.60% | 76.62% | Useful/Caution (Amber) |
| 2012 | Crott, R., Versteegh, M., & Uyl-de-Groot, C. (2013). An assessment of the external validity of mapping QLQ-C30 to EQ-5D preferences. Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation, 22(5), 1045–1054. https://doi.org/10.1007/s11136-012-0220-10 | EORTC QLQ-C30 | EQ-5D | Oncology | 0.701 | 0.139 | 32.93% | 74.20% | Useful/Caution (Amber) |
| 2011 | Starkie, H. J., Briggs, A. H., Chambers, M. G., & Jones, P. (2011). Predicting EQ-5D values using the SGRQ. Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research, 14(2), 354–360. https://doi.org/10.1016/j.jval.2010.09.011 | SGRQ | EQ-5D | Respiratory | 0.471 | 0.172 | 20.50% | 35.58% | Poor (Red) |
| 2011 | Rowen, D., Brazier, J., Tsuchiya, A., & Alava, M. H. (2012). Valuing states from multiple measures on the same visual analogue sale: a feasibility study. Health economics, 21(6), 715–729. https://doi.org/10.1002/hec.1754 | ICECAP | HUI2 | Mixed Disease Types | 0.930 | 0.003 | 81.04% | 94.08% | Ready to Use (Green) |
| 2011 | Rowen, D., Brazier, J., Tsuchiya, A., & Alava, M. H. (2012). Valuing states from multiple measures on the same visual analogue sale: a feasibility study. Health economics, 21(6), 715–729. https://doi.org/10.1002/hec.1753 | OPUS | HUI2 | Mixed Disease Types | 0.940 | 0.002 | 82.82% | 94.85% | Ready to Use (Green) |
| 2011 | Rowen, D., Brazier, J., Tsuchiya, A., & Alava, M. H. (2012). Valuing states from multiple measures on the same visual analogue sale: a feasibility study. Health economics, 21(6), 715–729. https://doi.org/10.1002/hec.1752 | AQL-5D | HUI2 | Mixed Disease Types | 0.940 | 0.001 | 82.81% | 94.21% | Ready to Use (Green) |
| 2011 | Rowen, D., Brazier, J., Tsuchiya, A., & Alava, M. H. (2012). Valuing states from multiple measures on the same visual analogue sale: a feasibility study. Health economics, 21(6), 715–729. https://doi.org/10.1002/hec.1751 | SF-6D | HUI2 | Mixed Disease Types | 0.970 | 0.001 | 84.30% | 95.66% | Ready to Use (Green) |
| 2011 | Rowen, D., Brazier, J., Tsuchiya, A., & Alava, M. H. (2012). Valuing states from multiple measures on the same visual analogue sale: a feasibility study. Health economics, 21(6), 715–729. https://doi.org/10.1002/hec.1750 | EQ-5D | HUI2 | Mixed Disease Types | 0.910 | 0.003 | 80.53% | 93.29% | Ready to Use (Green) |
| 2011 | Rowen, D., Brazier, J., Tsuchiya, A., & Alava, M. H. (2012). Valuing states from multiple measures on the same visual analogue sale: a feasibility study. Health economics, 21(6), 715–729. https://doi.org/10.1002/hec.1749 | ICECAP | SF-6D | Mixed Disease Types | 0.970 | 0.001 | 82.67% | 95.32% | Ready to Use (Green) |
| 2011 | Rowen, D., Brazier, J., Tsuchiya, A., & Alava, M. H. (2012). Valuing states from multiple measures on the same visual analogue sale: a feasibility study. Health economics, 21(6), 715–729. https://doi.org/10.1002/hec.1748 | OPUS | SF-6D | Mixed Disease Types | 0.970 | 0.001 | 81.69% | 95.20% | Ready to Use (Green) |
| 2011 | Rowen, D., Brazier, J., Tsuchiya, A., & Alava, M. H. (2012). Valuing states from multiple measures on the same visual analogue sale: a feasibility study. Health economics, 21(6), 715–729. https://doi.org/10.1002/hec.1747 | AQL-5D | SF-6D | Mixed Disease Types | 0.970 | 0.001 | 83.22% | 95.04% | Ready to Use (Green) |
| 2011 | Rowen, D., Brazier, J., Tsuchiya, A., & Alava, M. H. (2012). Valuing states from multiple measures on the same visual analogue sale: a feasibility study. Health economics, 21(6), 715–729. https://doi.org/10.1002/hec.1746 | HUI2 | SF-6D | Mixed Disease Types | 0.970 | 0.001 | 82.45% | 95.26% | Ready to Use (Green) |
| 2011 | Rowen, D., Brazier, J., Tsuchiya, A., & Alava, M. H. (2012). Valuing states from multiple measures on the same visual analogue sale: a feasibility study. Health economics, 21(6), 715–729. https://doi.org/10.1002/hec.1745 | EQ-5D | SF-6D | Mixed Disease Types | 0.970 | 0.001 | 80.81% | 95.85% | Ready to Use (Green) |
| 2011 | Rowen, D., Brazier, J., Tsuchiya, A., & Alava, M. H. (2012). Valuing states from multiple measures on the same visual analogue sale: a feasibility study. Health economics, 21(6), 715–729. https://doi.org/10.1002/hec.1744 | ICECAP | EQ-5D | Mixed Disease Types | 0.930 | 0.003 | 82.38% | 93.37% | Ready to Use (Green) |
| 2011 | Rowen, D., Brazier, J., Tsuchiya, A., & Alava, M. H. (2012). Valuing states from multiple measures on the same visual analogue sale: a feasibility study. Health economics, 21(6), 715–729. https://doi.org/10.1002/hec.1743 | OPUS | EQ-5D | Mixed Disease Types | 0.940 | 0.002 | 81.88% | 93.84% | Ready to Use (Green) |
| 2011 | Rowen, D., Brazier, J., Tsuchiya, A., & Alava, M. H. (2012). Valuing states from multiple measures on the same visual analogue sale: a feasibility study. Health economics, 21(6), 715–729. https://doi.org/10.1002/hec.1742 | AQL-5D | EQ-5D | Mixed Disease Types | 0.940 | 0.001 | 82.46% | 94.63% | Ready to Use (Green) |
| 2011 | Rowen, D., Brazier, J., Tsuchiya, A., & Alava, M. H. (2012). Valuing states from multiple measures on the same visual analogue sale: a feasibility study. Health economics, 21(6), 715–729. https://doi.org/10.1002/hec.1741 | HUI2 | EQ-5D | Mixed Disease Types | 0.910 | 0.003 | 81.55% | 93.54% | Ready to Use (Green) |
| 2011 | Rowen, D., Brazier, J., Tsuchiya, A., & Alava, M. H. (2012). Valuing states from multiple measures on the same visual analogue sale: a feasibility study. Health economics, 21(6), 715–729. https://doi.org/10.1002/hec.1740 | SF-6D | EQ-5D | Mixed Disease Types | 0.970 | 0.001 | 82.87% | 95.45% | Ready to Use (Green) |
| 2011 | Parker, M., Haycox, A., & Graves, J. (2011). Estimating the relationship between preference-based generic utility instruments and disease-specific quality-of-life measures in severe chronic constipation: challenges in practice. PharmacoEconomics, 29(8), 719–730. https://doi.org/10.2165/11588360-000000000-00000 | PAC-QOL & PAC-SYM | EQ-5Q | Stomach & Bowel | 0.320 | 0.144 | 30.53% | 14.83% | Poor (Red) |
| 2011 | Norlin, J. M., Steen Carlsson, K., Persson, U., & Schmitt-Egenolf, M. (2012). Analysis of three outcome measures in moderate to severe psoriasis: a registry-based study of 2450 patients. The British journal of dermatology, 166(4), 797–802. https://doi.org/10.1111/j.1365-2133.2011.10778.x | DLQI | EQ-5D | Dermatology | 0.320 | 0.199 | 12.27% | 15.21% | Poor (Red) |
| 2011 | Kaambwa, B., Billingham, L., & Bryan, S. (2013). Mapping utility scores from the Barthel index. The European journal of health economics : HEPAC : health economics in prevention and care, 14(2), 231–241. https://doi.org/10.1007/s10198-011-0364-5 | Barthel index | EQ-5D | Mixed Disease Types | 0.212 | 0.270 | 2.44% | 6.63% | Poor (Red) |
| 2011 | Jia, H., Zack, M. M., Moriarty, D. G., & Fryback, D. G. (2011). Predicting the EuroQol Group's EQ-5D index from CDC's "Healthy Days" in a US sample. Medical decision making : an international journal of the Society for Medical Decision Making, 31(1), 174–185. https://doi.org/10.1177/0272989X10364845 | Healthy Days, developed by the Centers for Disease Control and Preventions | EQ-5D | Mixed Disease Types | 0.519 | 0.106 | 46.81% | 44.30% | Poor (Red) |
| 2011 | Hawton, A., Green, C., Telford, C. J., Wright, D. E., & Zajicek, J. P. (2012). The use of multiple sclerosis condition-specific measures to inform health policy decision-making: mapping from the MSWS-12 to the EQ-5D. Multiple sclerosis (Houndmills, Basingstoke, England), 18(6), 853–861. https://doi.org/10.1177/1352458511429319 | MSWS-12 | EQ-5D | CNS | 0.361 | 0.198 | 13.25% | 19.82% | Poor (Red) |
| 2010 | Xie, F., Pullenayegum, E. M., Li, S. C., Hopkins, R., Thumboo, J., & Lo, N. N. (2010). Use of a disease-specific instrument in economic evaluations: mapping WOMAC onto the EQ-5D utility index. Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research, 13(8), 873–878. https://doi.org/10.1111/j.1524-4733.2010.00770.x | WOMAC | EQ-5D | Musculoskeletal | 0.449 | 0.095 | 51.40% | 32.40% | Poor (Red) |
| 2010 | Wolfe, F., Michaud, K., & Wallenstein, G. (2010). Scale characteristics and mapping accuracy of the US EQ-5D, UK EQ-5D, and SF-6D in patients with rheumatoid arthritis. The Journal of rheumatology, 37(8), 1615–1625. https://doi.org/10.3899/jrheum.100044 | HAQ | SF-6D | Rheumatology | 0.720 | 0.070 | 61.54% | 76.59% | Ready to Use (Green) |
| 2010 | Wolfe, F., Michaud, K., & Wallenstein, G. (2010). Scale characteristics and mapping accuracy of the US EQ-5D, UK EQ-5D, and SF-6D in patients with rheumatoid arthritis. The Journal of rheumatology, 37(8), 1615–1625. https://doi.org/10.3899/jrheum.100043 | HAQ | EQ-5D | Rheumatology | 0.680 | 0.100 | 49.29% | 70.51% | Useful/Caution (Amber) |
| 2010 | Rivero-Arias, O., Ouellet, M., Gray, A., Wolstenholme, J., Rothwell, P. M., & Luengo-Fernandez, R. (2010). Mapping the modified Rankin scale (mRS) measurement into the generic EuroQol (EQ-5D) health outcome. Medical decision making : an international journal of the Society for Medical Decision Making, 30(3), 341–354. https://doi.org/10.1177/0272989X09349961 | mRS | EQ-5D | Cardiovascular | 0.450 | 0.041 | 70.93% | 31.69% | Useful/Caution (Amber) |
| 2010 | Jang, R. W., Isogai, P. K., Mittmann, N., Bradbury, P. A., Shepherd, F. A., Feld, R., & Leighl, N. B. (2010). Derivation of utility values from European Organization for Research and Treatment of Cancer Quality of Life-Core 30 questionnaire values in lung cancer. Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer, 5(12), 1953–1957. https://doi.org/10.1097/jto.0b013e3181f77a6a | EORTC QLQ-C30 | EQ-5D | Oncology | 0.580 | 0.020 | 77.30% | 54.26% | Ready to Use (Green) |
| 2010 | Goldsmith, K. A., Dyer, M. T., Buxton, M. J., & Sharples, L. D. (2010). Mapping of the EQ-5D index from clinical outcome measures and demographic variables in patients with coronary heart disease. Health and quality of life outcomes, 8, 54. https://doi.org/10.1186/1477-7525-8-54 | Clinical outcome measures and demographic variables, including Seattle Angina Questionnaire | EQ-5D | Cardiovascular | 0.490 | 0.169 | 21.08% | 39.05% | Poor (Red) |
| 2010 | Crott, R., & Briggs, A. (2010). Mapping the QLQ-C30 quality of life cancer questionnaire to EQ-5D patient preferences. The European journal of health economics : HEPAC : health economics in prevention and care, 11(4), 427–434. https://doi.org/10.1007/s10198-010-0233-7 | EORTC QLQ-C30 | EQ-5D | Oncology | 0.801 | 0.096 | 50.68% | 85.04% | Useful/Caution (Amber) |
| 2009 | Rowen, D., Brazier, J., & Roberts, J. (2009). Mapping SF-36 onto the EQ-5D index: how reliable is the relationship?. Health and quality of life outcomes, 7, 27. https://doi.org/10.1186/1477-7525-7-27 | SF-36 | EQ-5D | Mixed Disease Types | 0.710 | 0.030 | 74.87% | 74.70% | Ready to Use (Green) |
| 2009 | Kontodimopoulos, N., Aletras, V. H., Paliouras, D., & Niakas, D. (2009). Mapping the cancer-specific EORTC QLQ-C30 to the preference-based EQ-5D, SF-6D, and 15D instruments. Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research, 12(8), 1151–1157. https://doi.org/10.1111/j.1524-4733.2009.00569.x | EORTC QLQ-C30 | SF-6D | Oncology | 0.833 | 0.038 | 72.99% | 87.53% | Ready to Use (Green) |
| 2009 | Kontodimopoulos, N., Aletras, V. H., Paliouras, D., & Niakas, D. (2009). Mapping the cancer-specific EORTC QLQ-C30 to the preference-based EQ-5D, SF-6D, and 15D instruments. Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research, 12(8), 1151–1157. https://doi.org/10.1111/j.1524-4733.2009.00569.x | EORTC QLQ-C30 | EQ-5D | Oncology | 0.611 | 0.192 | 14.38% | 59.79% | Useful/Caution (Amber) |
| 2009 | Kontodimopoulos, N., Aletras, V. H., Paliouras, D., & Niakas, D. (2009). Mapping the cancer-specific EORTC QLQ-C30 to the preference-based EQ-5D, SF-6D, and 15D instruments. Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research, 12(8), 1151–1157. https://doi.org/10.1111/j.1524-4733.2009.00569.x | EORTC QLQ-C30 | 15D | Oncology | 0.909 | 0.050 | 69.64% | 92.62% | Ready to Use (Green) |
| 2009 | Chuang, L. H., & Kind, P. (2009). Converting the SF-12 into the EQ-5D: an empirical comparison of methodologies. PharmacoEconomics, 27(6), 491–505. https://doi.org/10.2165/00019053-200927060-00005 | SF-12 | EQ-5D | Mixed Disease Types | 0.656 | 0.011 | 80.42% | 67.06% | Ready to Use (Green) |
| 2008 | Barton, G. R., Sach, T. H., Jenkinson, C., Avery, A. J., Doherty, M., & Muir, K. R. (2008). Do estimates of cost-utility based on the EQ-5D differ from those based on the mapping of utility scores?. Health and quality of life outcomes, 6, 51. https://doi.org/10.1186/1477-7525-6-51 | WOMAC | EQ-5D | Musculoskeletal | 0.313 | 0.180 | 17.62% | 14.74% | Poor (Red) |
| 2005 | Longworth, L., Buxton, M. J., Sculpher, M., & Smith, D. H. (2005). Estimating utility data from clinical indicators for patients with stable angina. The European journal of health economics : HEPAC : health economics in prevention and care, 6(4), 347–353. https://doi.org/10.1007/s10198-005-0309-y | Breathlessness grade and Canadian Cardiovascular Society (CSS) classification of angina and number of drug classes used | EQ-5D | Cardiovascular | 0.370 | 0.227 | 6.75% | 21.13% | Poor (Red) |