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Does selecting covariates using factor analysis in mapping algorithms improve predictive accuracy? A case of predicting EQ-5D-5L and SF-6D utilities from the Women's Health Questionnaire

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dc.contributor.authorKaambwa, B.
dc.contributor.authorSmith, C.
dc.contributor.authorde Lacey, S.
dc.contributor.authorRatcliffe, J.
dc.date.issued2018
dc.descriptionData source: Supporting materials, https://doi.org/10.1016/j.jval.2018.01.020
dc.description.abstractBACKGROUND:In addition to theoretical justifications, many statistical methods have been used for selecting covariates to include in algorithms mapping nonutility measures onto utilities. However, it is not clear whether using exploratory factor analysis (EFA) as one such method improves the predictive ability of these algorithms. OBJECTIVE: This question is addressed within the context of mapping a non-utility-based outcome, the core 23-item Women's Health Questionnaire (WHQ-23), onto two utility instruments: five-level EuroQol five-dimensional questionnaire (EQ-5D-5L) and the six-dimensional health state short form (derived from short form 36 health survey) (SF-6D). METHODS: Data on all three outcomes were collected from 455 women from the Australian general population participating in a study assessing attitudes toward in vitro fertilization. Statistical methods for selecting covariates included stepwise regression (SW), including all covariates (Include all), multivariable fractional polynomial (MFP), and EFA. The predictive accuracy of 108 regression models was assessed using five criteria: mean absolute error, root mean squared error, correlation, distribution of predicted utilities, and proportion of predictions with absolute errors of less than 0.0.5. Validation of "primary" models was carried out on random samples of the in vitro fertilization study. RESULTS: The best results for EQ-5D-5L and SF-6D predictions were obtained from models using SW, "Include all," and MFP covariate-selection approaches. Root mean squared error (0.0762-0.1434) and mean absolute error (0.0590-0.0924) estimates for these models were within the range of published estimates. EFA was outperformed by other covariate-selection methods. CONCLUSIONS: It is possible to predict valid utilities from the WHQ-23 using regression methods based on SW, "Include all," and MFP covariate-selection techniques.
dc.description.statementofresponsibilityBillingsley Kaambwa, Caroline Smith, Sheryl de Lacey, Julie Ratcliffe
dc.identifier.citationValue in Health, 2018; 21(10):1205-1217
dc.identifier.doi10.1016/j.jval.2018.01.020
dc.identifier.issn1098-3015
dc.identifier.issn1524-4733
dc.identifier.orcidKaambwa, B. [0000-0002-2128-3404]
dc.identifier.orcidRatcliffe, J. [0000-0001-7365-1988]
dc.identifier.urihttp://hdl.handle.net/2440/123186
dc.language.isoen
dc.publisherElsevier
dc.relation.granthttp://purl.org/au-research/grants/nhmrc/1003661
dc.rights© 2018, ISPOR -The Professional Society for Health Economics and Outcomes Research. Published by Elsevier Inc.
dc.source.urihttps://doi.org/10.1016/j.jval.2018.01.020
dc.subjectEQ-5D-5L; factor analysisl mapping; SF-6D; utilities; WHQ-23
dc.titleDoes selecting covariates using factor analysis in mapping algorithms improve predictive accuracy? A case of predicting EQ-5D-5L and SF-6D utilities from the Women's Health Questionnaire
dc.typeJournal article
pubs.publication-statusPublished

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