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QALYs in adults with cerebral palsy: Mapping from the San Martin Scale onto the EQ-5D-5L instrument

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  • Diana M Nova Díaz
  • Aritz Adin
  • Eduardo Sánchez Iriso

Abstract

Responses on health-related quality of life measured by disease-specific instruments can be mapped onto the EQ-5D-5L to estimate utility values for economic evaluation. San Martin´s Quality of Life Scale (St. MQoL-S) is a preferred measure to obtain health outcomes in adults with cerebral palsy. Nevertheless, it lacks a preference-based health utility score for estimating quality-adjusted life years (QALYs).

Suggested Citation

  • Diana M Nova Díaz & Aritz Adin & Eduardo Sánchez Iriso, 2024. "QALYs in adults with cerebral palsy: Mapping from the San Martin Scale onto the EQ-5D-5L instrument," Working Papers 2024-07, FEDEA.
    • Handle: RePEc:fda:fdaddt:2024-07
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    1. Taniguchi-Matsuoka, Ayako & Shimoda, Yoshiyuki & Sugiyama, Minami & Kurokawa, Yusuke & Matoba, Haruka & Yamasaki, Tomoya & Morikuni, Taro & Yamaguchi, Yohei, 2020. "Evaluating Japan’s national greenhouse gas reduction policy using a bottom-up residential end-use energy simulation model," Applied Energy, Elsevier, vol. 279(C).
    2. Weron, Rafał, 2014. "Electricity price forecasting: A review of the state-of-the-art with a look into the future," International Journal of Forecasting, Elsevier, vol. 30(4), pages 1030-1081.
    3. Sun, Maoran & Han, Changyu & Nie, Quan & Xu, Jingying & Zhang, Fan & Zhao, Qunshan, 2022. "Understanding Building Energy Efficiency with Administrative and Emerging Urban Big Data by Deep Learning in Glasgow," OSF Preprints g8p4f, Center for Open Science.
    4. Jean Gaston Tamba & Salom Ndjakomo Essiane & Emmanuel Flavian Sapnken & Francis Djanna Koffi & Jean Luc Nsouand l & Bozidar Soldo & Donatien Njomo, 2018. "Forecasting Natural Gas: A Literature Survey," International Journal of Energy Economics and Policy, Econjournals, vol. 8(3), pages 216-249.
    5. Andreea Valeria Vesa & Tudor Cioara & Ionut Anghel & Marcel Antal & Claudia Pop & Bogdan Iancu & Ioan Salomie & Vasile Teodor Dadarlat, 2020. "Energy Flexibility Prediction for Data Center Engagement in Demand Response Programs," Sustainability, MDPI, vol. 12(4), pages 1-23, February.
    6. Zhao, Yang & Li, Jianping & Yu, Lean, 2017. "A deep learning ensemble approach for crude oil price forecasting," Energy Economics, Elsevier, vol. 66(C), pages 9-16.
    7. Rafal Weron, 2006. "Modeling and Forecasting Electricity Loads and Prices: A Statistical Approach," HSC Books, Hugo Steinhaus Center, Wroclaw University of Technology, number hsbook0601, December.
    8. Zhou, Kaile & Fu, Chao & Yang, Shanlin, 2016. "Big data driven smart energy management: From big data to big insights," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 215-225.
    9. Zhao, Hai-xiang & Magoulès, Frédéric, 2012. "A review on the prediction of building energy consumption," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(6), pages 3586-3592.
    10. Singh, Sanjeet & Bansal, Pooja & Hosen, Mosharrof & Bansal, Sanjeev K., 2023. "Forecasting annual natural gas consumption in USA: Application of machine learning techniques- ANN and SVM," Resources Policy, Elsevier, vol. 80(C).
    11. Tiwari, Aviral Kumar & Sharma, Gagan Deep & Rao, Amar & Hossain, Mohammad Razib & Dev, Dhairya, 2024. "Unraveling the crystal ball: Machine learning models for crude oil and natural gas volatility forecasting," Energy Economics, Elsevier, vol. 134(C).
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