Kim Rand-Hendriksen1, Juan Manuel Ramos-Goñi2, Liv Ariane Augestad3, Nan Luo4. 1. Health Services Research Centre, Akershus University Hospital, Lørenskog, Norway; Department of Health Management and Health Economics, University of Oslo, Oslo, Norway. Electronic address: kim.rand-hendriksen@medisin.uio.no. 2. Executive Office, EuroQol Research Foundation, Rotterdam, The Netherlands; Red de Investigación en Servicios de Salud en Enfermedades Crónicas (REDISSEC), Madrid, Spain. 3. Health Services Research Centre, Akershus University Hospital, Lørenskog, Norway; Department of Health Management and Health Economics, University of Oslo, Oslo, Norway. 4. Saw Swee Hock School of Public Health, National University of Singapore, Singapore.
Abstract
BACKGROUND: The conventional method for modeling of the five-level EuroQol five-dimensional questionnaire (EQ-5D-5L) health state values in national valuation studies is an additive 20-parameter main-effects regression model. Statistical models with many parameters are at increased risk of overfitting-fitting to noise and measurement error, rather than the underlying relationship. OBJECTIVES: To compare the 20-parameter main-effects model to simplified, nonlinear, multiplicative regression models in terms of how accurately they predict mean values of out-of-sample health states. METHODS: We used data from the Spanish, Singaporean, and Chinese EQ-5D-5L valuation studies. Four models were compared: an 8-parameter model with single parameter per dimension, multiplied by cross-dimensional parameters for levels 2, 3, and 4; 9- and 11-parameter extensions with handling of differences in the wording of level 5; and the "standard" additive 20-parameter model. Fixed- and random-intercept variants of all models were tested using two cross-validation methods: leave-one-out at the level of valued health states, and of health state blocks used in EQ-5D-5L valuation studies. Mean absolute error, Lin concordance correlation coefficient, and Pearson R between observed health state means and out-of-sample predictions were compared. RESULTS:Predictive accuracy was generally best using random intercepts. The 8-, 9-, and 11-parameter models outperformed the 20-parameter model in predicting out-of-sample health states. CONCLUSIONS: Simplified nonlinear regression models look promising and should be investigated further using other EQ-5D-5L data sets. To reduce the risk of overfitting, cross-validation is recommended to inform model selection in future EQ-5D valuation studies.
RCT Entities:
BACKGROUND: The conventional method for modeling of the five-level EuroQol five-dimensional questionnaire (EQ-5D-5L) health state values in national valuation studies is an additive 20-parameter main-effects regression model. Statistical models with many parameters are at increased risk of overfitting-fitting to noise and measurement error, rather than the underlying relationship. OBJECTIVES: To compare the 20-parameter main-effects model to simplified, nonlinear, multiplicative regression models in terms of how accurately they predict mean values of out-of-sample health states. METHODS: We used data from the Spanish, Singaporean, and Chinese EQ-5D-5L valuation studies. Four models were compared: an 8-parameter model with single parameter per dimension, multiplied by cross-dimensional parameters for levels 2, 3, and 4; 9- and 11-parameter extensions with handling of differences in the wording of level 5; and the "standard" additive 20-parameter model. Fixed- and random-intercept variants of all models were tested using two cross-validation methods: leave-one-out at the level of valued health states, and of health state blocks used in EQ-5D-5L valuation studies. Mean absolute error, Lin concordance correlation coefficient, and Pearson R between observed health state means and out-of-sample predictions were compared. RESULTS: Predictive accuracy was generally best using random intercepts. The 8-, 9-, and 11-parameter models outperformed the 20-parameter model in predicting out-of-sample health states. CONCLUSIONS: Simplified nonlinear regression models look promising and should be investigated further using other EQ-5D-5L data sets. To reduce the risk of overfitting, cross-validation is recommended to inform model selection in future EQ-5D valuation studies.
Authors: Mingzhu Su; Xingxing Hua; Jialin Wang; Nengliang Yao; Deli Zhao; Weidong Liu; Yuewei Zou; Roger Anderson; Xiaojie Sun Journal: Qual Life Res Date: 2018-10-29 Impact factor: 4.147
Authors: Red Thaddeus D Miguel; Adovich S Rivera; Kent Jason G Cheng; Kim Rand; Fredrick Dermawan Purba; Nan Luo; Ma-Ann Zarsuelo; Anne Julienne Genuino-Marfori; Irene Florentino-Fariñas; Anna Melissa Guerrero; Hilton Y Lam Journal: Qual Life Res Date: 2022-05-09 Impact factor: 3.440