Dalziel, K., Segal, L., & Mortimer, D. (2008). Review of Australian health economic evaluation – 245 interventions: What can we say about cost effectiveness? Cost Effectiveness and Resource Allocation, 6(1), 9.

Article  PubMed  PubMed Central  Google Scholar 

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.

Article  PubMed  PubMed Central  Google Scholar 

Prieto, L., & Sacristán, J. A. (2003). Problems and solutions in calculating quality-adjusted life years (QALYs). Health and Quality of Life Outcomes, 1, 80.

Article  PubMed  PubMed Central  Google Scholar 

Richardson, J., Iezzi, A., & Khan, M. A. (2015). Why do multi-attribute utility instruments produce different utilities: The relative importance of the descriptive systems, scale and ‘micro-utility’ effects. Quality of life Research: An International Journal of Quality of life Aspects of Treatment care and Rehabilitation, 24(8), 2045–2053.

Article  PubMed  Google Scholar 

Wells, G. A., Russell, A. S., Haraoui, B., Bissonnette, R., & Ware, C. F. (2011). Validity of quality of Life Measurement Tools — from generic to Disease-specific. The Journal of Rheumatology, 88, 2.

PubMed  Google Scholar 

Ware, J. E. Jr., Gandek, B., Guyer, R., & Deng, N. (2016). Standardizing disease-specific quality of life measures across multiple chronic conditions: Development and initial evaluation of the QOL Disease Impact Scale (QDIS®). Health and Quality of Life Outcomes, 14, 84.

Article  PubMed  PubMed Central  Google Scholar 

Kularatna, S., Byrnes, J., Chan, Y. K., Carrington, M. J., Stewart, S., & Scuffham, P. A. (2017). Comparison of contemporaneous responses for EQ-5D-3L and Minnesota living with Heart Failure; a case for disease specific multiattribute utility instrument in cardiovascular conditions. International Journal of Cardiology, 227, 172–176.

Article  PubMed  Google Scholar 

Cichosz, S. L., Ehlers, L. H., & Hejlesen, O. (2016). Health effectiveness and cost-effectiveness of telehealthcare for heart failure: Study protocol for a randomized controlled trial. Trials, 17(1), 1–6.

Article  Google Scholar 

Kularatna, S., Chen, G., Senanayake, S., Hettiarachchi, R., Parsonage, W., Norman, R., et al. (2022). Australian Health Utility Value set for MacNew-7D heart disease-specific measure. Heart Lung and Circulation, 31, S71.

Article  Google Scholar 

Kularatna, S., Rowen, D., Mukuria, C., McPhail, S., Chen, G., Mulhern, B., et al. (2022). Development of a preference-based heart disease-specific health state classification system using MacNew heart disease-related quality of life instrument. Quality of Life Research, 31(1), 257–268.

Article  PubMed  Google Scholar 

World Health Organization. Cardiovascular diseases (CVDs) - Key Facts: WHO (2021). [ https://www.who.int/news-room/fact-sheets/detail/cardiovascular-diseases-(cvds).

Chen, G., Garcia-Gordillo, M. A., Collado-Mateo, D., del Pozo-Cruz, B., Adsuar, J. C., Cordero-Ferrera, J. M., et al. (2018). Converting Parkinson-Specific scores into Health State Utilities to assess cost-utility analysis. The Patient - Patient-Centered Outcomes Research, 11(6), 665–675.

Article  PubMed  Google Scholar 

Chen, G., McKie, J., Khan, M. A., & Richardson, J. R. (2015). Deriving health utilities from the macnew heart disease quality of life questionnaire. European Journal of Cardiovascular Nursing, 14(5), 405–415.

Article  PubMed  Google Scholar 

Kularatna, S., Senanayake, S., Chen, G., & Parsonage, W. (2020). Mapping the Minnesota living with heart failure questionnaire (MLHFQ) to EQ-5D-5L in patients with heart failure. Health and Quality of Life Outcomes, 18(1), 1–12.

Article  Google Scholar 

Green, C. P., Dennis, C. B. P., Bresnahan, R., & Spertus, J. A. (2000). Development and evaluation of the Kansas City Cardiomyopathy Questionnaire: A new health status measure for heart failure. Journal of the American College of Cardiology, 35(5), 1245–1255.

Article  CAS  PubMed  Google Scholar 

Spertus, J. A., Jones, P. G., Sandhu, A. T., & Arnold, S. V. (2020). Interpreting the Kansas City Cardiomyopathy Questionnaire in clinical trials and clinical care: JACC state-of-the-art review. Journal of the American College of Cardiology, 76(20), 2379–2390.

Article  PubMed  Google Scholar 

Bilbao, A., Escobar, A., García-Perez, L., Navarro, G., & Quirós, R. (2016). The Minnesota living with heart failure questionnaire: Comparison of different factor structures. Health and Quality of Life Outcomes, 14, 23.

Article  PubMed  PubMed Central  Google Scholar 

Thomas, M., Jones, P. G., Arnold, S. V., & Spertus, J. A. (2021). Interpretation of the Seattle Angina Questionnaire as an Outcome measure in clinical trials and clinical care: A review. JAMA Cardiology, 6(5), 593–599.

Article  PubMed  PubMed Central  Google Scholar 

Kularatna, S., Senanayake, S., Chen, G., & Parsonage, W. (2020). Mapping the Minnesota living with heart failure questionnaire (MLHFQ) to EQ-5D-5L in patients with heart failure. Health and Quality of Life Outcomes, 18(1), 115.

Article  PubMed  PubMed Central  Google Scholar 

Statistics CiRaMfO Root mean square error (RMSE) 2019 [ https://ec.europa.eu/eurostat/cros/content/root-mean-square-error-rmse_en#:~:text=The%20Root%20mean%20square%20erro,of%20variance%20and%20squared%20Bias.

Neilson, A. R., Jones, G. T., Macfarlane, G. J., Pathan, E. M., McNamee, P., & Generating (2022). EQ-5D-5L health utility scores from BASDAI and BASFI: A mapping study in patients with axial spondyloarthritis using longitudinal UK registry data. The European Journal of Health Economics, 23(8), 1357–1369.

Article  PubMed  PubMed Central  Google Scholar 

Brazier, J. E., Yang, Y., Tsuchiya, A., & Rowen, D. L. (2010). A review of studies mapping (or cross walking) non-preference based measures of health to generic preference-based measures. The European Journal of Health Economics, 11, 215–225.

Article  PubMed  Google Scholar 

Meregaglia, M., Whittal, A., Nicod, E., & Drummond, M. (2020). Mapping’ Health State Utility values from non-preference-based measures: A systematic literature. Review in Rare Diseases PharmacoEconomics, 38(6), 557–574.

PubMed  Google Scholar 

Brazier, J., Czoski-Murray, C., Roberts, J., Brown, M., Symonds, T., & Kelleher, C. (2008). Estimation of a preference-based index from a condition-specific measure: The King’s Health Questionnaire. Medical Decision Making, 28(1), 113–126.

Article  PubMed  Google Scholar 

Yang, F., Devlin, N., & Luo, N. (2019). Impact of mapped EQ-5D utilities on cost-effectiveness analysis: In the case of dialysis treatments. The European Journal of Health Economics, 20, 99–105.

Article  PubMed  Google Scholar 

Youngerman, B. E., Mahajan, U. V., Dyster, T. G., Srinivasan, S., Halpern, C. H., McKhann, G. M., & Sheth, S. A. (2021). Cost-effectiveness analysis of responsive neurostimulation for drug‐resistant focal onset epilepsy. Epilepsia, 62(11), 2804–2813.

Article  PubMed  Google Scholar 

Trenaman, L., Stacey, D., Bryan, S., Taljaard, M., Hawker, G., Dervin, G., et al. (2017). Decision aids for patients considering total joint replacement: A cost-effectiveness analysis alongside a randomised controlled trial. Osteoarthritis and Cartilage, 25(10), 1615–1622.

Article  CAS  PubMed  Google Scholar 

King, R. D., Orhobor, O. I., & Taylor, C. C. (2021). Cross-validation is safe to use. Nature Machine Intelligence, 3(4), 276.

Article  Google Scholar 

Schaffer, C. (1993). Selecting a classification method by cross-validation. Machine Learning, 13, 135–143.

Article  Google Scholar 

Valsamis, E. M., Beard, D., Carr, A., Collins, G. S., Brealey, S., Rangan, A., et al. (2023). Mapping the Oxford shoulder score onto the EQ-5D utility index. Quality of life Research, 32(2), 507–518.

Article  PubMed  Google Scholar 

Doble, B., & Lorgelly, P. (2016). Mapping the EORTC QLQ-C30 onto the EQ-5D-3L: Assessing the external validity of existing mapping algorithms. Quality of Life Research, 25, 891–911.

Article  PubMed  Google Scholar 

Ali, F. M., Kay, R., Finlay, A. Y., Piguet, V., Kupfer, J., Dalgard, F., & Salek, M. S. (2017). Mapping of the DLQI scores to EQ-5D utility values using ordinal logistic regression. Quality of Life Research, 26, 3025–3034.

Article  PubMed  PubMed Central  Google Scholar 

Klapproth, C. P., van Bebber, J., Berlin, C. U., Gibbons, C. J., Valderas, J. M., Alain, L. (2020). Predicting EQ-5D Index Scores from the PROMIS-29 Pro le for the United Kingdom, France, and Germany.

Austin, D. E., Lee, D. S., Wang, C. X., Ma, S., Wang, X., Porter, J., & Wang, B. (2022). Comparison of machine learning and the regression-based EHMRG model for predicting early mortality in acute heart failure. International Journal of Cardiology, 365, 78–84.

Article  PubMed  Google Scholar 

Mortazavi, B. J., Downing, N. S., Bucholz, E. M., Dharmarajan, K., Manhapra, A., Li, S-X., et al. (2016). Analysis of machine learning techniques for heart failure readmissions. Circulation: Cardiovascular Quality and Outcomes, 9(6), 629–640.

PubMed