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

Abstract

Background: 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 Euro-Qol 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.

Mapping the Minnesota living with heart failure questionnaire (MLHFQ) to EQ-5D-5L in patients with heart failure

“…The MFP is a useful modelling technique to be used when the dependant and the independent variables have a non-linear relationship [38]. Superiority of MFP compared to other robust regression models has been demonstrated previously as well [57]. Secondly, this is the only available algorithm in the medical literature which convers HF specific quality of life scores in to EQ-5D-5 L utility scores.…”

Current Status of Research on the Mapping Function of Health Utility Values in the Asia Pacific Region: A Systematic Review

“…In our current review, few studies were targeted at a specific age group or gender. Among the retrieved literature, there were only 6 studies specific for women, 16,21,39,49,52,53 6 studies of people under the age of 18, 12,[26][27][28][29]60 and 3 studies specific for elderly people. 8,34,35 However, some diseases are closely related to age and gender (eg, inflammatory enteritis often occurs in young and middle-aged people, and female patients account for a large proportion of hyperthyroidism diseases).…”