An examination of machine learning to map non-preference based patient reported outcome measures to health state utility values
Non-preference-based patient-reported outcome measures (PROMs) are popular in health outcomes research. These measures, however, cannot be used to estimate health state utilities, limiting their usefulness for economic evaluations. Mapping PROMs to a multi-attribute utility instrument is one solution. While mapping is commonly conducted using econometric techniques, failing to specify the complex interactions between variables may lead to inaccurate prediction of utilities, resulting in inaccurate estimates of cost-effectiveness and suboptimal funding decisions. These issues can be addressed using machine learning. This paper evaluates the use of machine learning as a mapping tool. We adopt a comprehensive approach to compare six machine learning techniques with eight econometric techniques to map the Patient-Reported Outcomes Measurement Information System Global Health 10 (PROMIS-GH10) to the EuroQol five dimensions (EQ-5D-5L). Using data collected from 2,015 Australians, we find the least absolute shrinkage and selection operator (LASSO) model out-performed all machine learning techniques and the adjusted limited dependent variable mixture model (ALDVMM) out-performed all econometric techniques, with the LASSO performing better than ALDVMM. The variable selection feature of LASSO was then used to enhance the performance of the ALDVMM in a hybrid model Our analysis identifies the potential benefits and challenges of using machine learning techniques for mapping and offers important insights for future research.
Speaker
Associate Professor Kompal Sinha, Macquarie University.
Kompal Sinha is Associate Professor of Economics at Macquarie Business School, Macquarie University. She is serving as a member of the College of Experts at the Australian Research Council and is an editor for the Journal of Population Economics. A/Prof Sinhaleads the Health, Development, Inequality and Behavior research cluster at the Global Labor Organisation. She is also serving on the Health Economics Service Panel of the Australian Government Department of Health for the NSW Government.
Her research has been published in highly ranked economics journals including Health Economics, the Journal of Economic Behaviour and Organisation, the American Journal for Agricultural Economics, Social Science and Medicine, Macroeconomic Dynamics, Energy Economics, Review of Income and Wealth, the Journal of Biosocial Sciences, Value in Health, and the British Medical Bulletin. She has received more than A$1 million as a chief investigator on grants funded by the Australian Government, the World Bank, Cochlear, and the Department of Health, among others. A/Prof Sinha has previously worked at Monash University, Melbourne. She received her PhD in Economics from the Australian National University, Canberra, Australia.
Organised by
Centre for Health Economics, Monash Business School
As part of our centre's vibrant research culture, we host a weekly seminar series. Visiting and invited researchers present current research relating to the economics of health and wellbeing, and the healthcare sector. Visitors are welcome to join these sessions, where discussion and debate is encouraged.
Contact shannon.stanwell@monash.edu for more details.
Event Details
- Date:
- 11 May 2022 at 12:00 pm – 1:00 pm
- Venue:
- Virtual (Zoom)
- Categories:
- Health Economics; CHE Seminar
Description
Non-preference-based patient-reported outcome measures (PROMs) are popular in health outcomes research. These measures, however, cannot be used to estimate health state utilities, limiting their usefulness for economic evaluations. Mapping PROMs to a multi-attribute utility instrument is one solution. While mapping is commonly conducted using econometric techniques, failing to specify the complex interactions between variables may lead to inaccurate prediction of utilities, resulting in inaccurate estimates of cost-effectiveness and suboptimal funding decisions. These issues can be addressed using machine learning. This paper evaluates the use of machine learning as a mapping tool. We adopt a comprehensive approach to compare six machine learning techniques with eight econometric techniques to map the Patient-Reported Outcomes Measurement Information System Global Health 10 (PROMIS-GH10) to the EuroQol five dimensions (EQ-5D-5L). Using data collected from 2,015 Australians, we find the least absolute shrinkage and selection operator (LASSO) model out-performed all machine learning techniques and the adjusted limited dependent variable mixture model (ALDVMM) out-performed all econometric techniques, with the LASSO performing better than ALDVMM. The variable selection feature of LASSO was then used to enhance the performance of the ALDVMM in a hybrid model Our analysis identifies the potential benefits and challenges of using machine learning techniques for mapping and offers important insights for future research.
Speaker
Associate Professor Kompal Sinha, Macquarie University.
Kompal Sinha is Associate Professor of Economics at Macquarie Business School, Macquarie University. She is serving as a member of the College of Experts at the Australian Research Council and is an editor for the Journal of Population Economics. A/Prof Sinhaleads the Health, Development, Inequality and Behavior research cluster at the Global Labor Organisation. She is also serving on the Health Economics Service Panel of the Australian Government Department of Health for the NSW Government.
Her research has been published in highly ranked economics journals including Health Economics, the Journal of Economic Behaviour and Organisation, the American Journal for Agricultural Economics, Social Science and Medicine, Macroeconomic Dynamics, Energy Economics, Review of Income and Wealth, the Journal of Biosocial Sciences, Value in Health, and the British Medical Bulletin. She has received more than A$1 million as a chief investigator on grants funded by the Australian Government, the World Bank, Cochlear, and the Department of Health, among others. A/Prof Sinha has previously worked at Monash University, Melbourne. She received her PhD in Economics from the Australian National University, Canberra, Australia.
Organised by
Centre for Health Economics, Monash Business School
As part of our centre's vibrant research culture, we host a weekly seminar series. Visiting and invited researchers present current research relating to the economics of health and wellbeing, and the healthcare sector. Visitors are welcome to join these sessions, where discussion and debate is encouraged.
Contact shannon.stanwell@monash.edu for more details.