dc.date.accessioned | 2018-07-12T09:07:15Z | |
dc.date.available | 2018-07-12T09:07:15Z | |
dc.date.created | 2017-09-26T14:05:00Z | |
dc.date.issued | 2017 | |
dc.identifier.citation | Rand, Kim Ramos-Goñi, Juan Manuel Augestad, Liv Ariane Luo, Nan . Less Is More: Cross-Validation Testing of Simplified Nonlinear Regression Model Specifications for EQ-5D-5L Health State Values. Value in Health. 2017, 20(7), 945-952 | |
dc.identifier.uri | http://hdl.handle.net/10852/62241 | |
dc.description.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. | en_US |
dc.language | EN | |
dc.publisher | Blackwell Publishers | |
dc.title | Less Is More: Cross-Validation Testing of Simplified Nonlinear Regression Model Specifications for EQ-5D-5L Health State Values | en_US |
dc.type | Journal article | en_US |
dc.creator.author | Rand, Kim | |
dc.creator.author | Ramos-Goñi, Juan Manuel | |
dc.creator.author | Augestad, Liv Ariane | |
dc.creator.author | Luo, Nan | |
cristin.unitcode | 185,52,11,0 | |
cristin.unitname | Avdeling for helseledelse og helseøkonomi | |
cristin.ispublished | true | |
cristin.fulltext | preprint | |
cristin.qualitycode | 1 | |
dc.identifier.cristin | 1498376 | |
dc.identifier.bibliographiccitation | info:ofi/fmt:kev:mtx:ctx&ctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=Value in Health&rft.volume=20&rft.spage=945&rft.date=2017 | |
dc.identifier.jtitle | Value in Health | |
dc.identifier.volume | 20 | |
dc.identifier.issue | 7 | |
dc.identifier.startpage | 945 | |
dc.identifier.endpage | 952 | |
dc.identifier.doi | http://dx.doi.org/10.1016/j.jval.2017.03.013 | |
dc.identifier.urn | URN:NBN:no-64830 | |
dc.type.document | Tidsskriftartikkel | en_US |
dc.source.issn | 1098-3015 | |
dc.identifier.fulltext | Fulltext https://www.duo.uio.no/bitstream/handle/10852/62241/2/pre_version4922.pdf | |
dc.type.version | SubmittedVersion | |
dc.relation.project | NFR/213127 | |