Does using factor analysis to select covariates in mapping algorithms improve predictive accuracy? A case of mapping the core 23-item Women’s Health Questionnaire onto the EQ-5D-5L and SF-6D

B. Kaambwa; S. de Lacey; C. Smith; J. Ratcliffe

doi:10.1016/j.jval.2017.08.052

Does using factor analysis to select covariates in mapping algorithms improve predictive accuracy? A case of mapping the core 23-item Women’s Health Questionnaire onto the EQ-5D-5L and SF-6D

B. Kaambwa, S. de Lacey, C. Smith, J. Ratcliffe

Research output: Contribution to journal › Meeting Abstract › peer-review

Abstract

Objectives: In addition to theoretical justifications, many statistical methods have been used for selecting covariates to include in algorithms mapping non-utility 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. 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: EuroQoL 5 Dimensions 5 Level (EQ-5D-5L) and the Short Form 6 Dimensions (SF-6D).

Original language	English
Pages (from-to)	A406-A407
Number of pages	2
Journal	Value in Health
Volume	20
Issue number	9
DOIs	https://doi.org/10.1016/j.jval.2017.08.052
Publication status	Published - Oct 2017
Event	ISPOR 20th Annual European Congress - Glasgow, United Kingdom Duration: 4 Nov 2017 → 8 Nov 2017

Keywords

EQ-5D-5L
Factor analysis
Mapping
SF-6D
Utilities
WHQ-23

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Cite this

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title = "Does using factor analysis to select covariates in mapping algorithms improve predictive accuracy? A case of mapping the core 23-item Women{\textquoteright}s Health Questionnaire onto the EQ-5D-5L and SF-6D",

abstract = "Objectives: In addition to theoretical justifications, many statistical methods have been used for selecting covariates to include in algorithms mapping non-utility 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. This question is addressed within the context of mapping a non-utility based outcome, the core 23-item Women{\textquoteright}s Health Questionnaire (WHQ-23), onto two utility instruments: EuroQoL 5 Dimensions 5 Level (EQ-5D-5L) and the Short Form 6 Dimensions (SF-6D).",

keywords = "EQ-5D-5L, Factor analysis, Mapping, SF-6D, Utilities, WHQ-23",

author = "B. Kaambwa and {de Lacey}, S. and C. Smith and J. Ratcliffe",

year = "2017",

month = oct,

doi = "10.1016/j.jval.2017.08.052",

language = "English",

volume = "20",

pages = "A406--A407",

journal = "Value in Health",

issn = "1098-3015",

publisher = "Elsevier Ltd.",

number = "9",

note = "ISPOR 20th Annual European Congress ; Conference date: 04-11-2017 Through 08-11-2017",

}

Does using factor analysis to select covariates in mapping algorithms improve predictive accuracy? A case of mapping the core 23-item Women’s Health Questionnaire onto the EQ-5D-5L and SF-6D. / Kaambwa, B.; de Lacey, S.; Smith, C. et al.
In: Value in Health, Vol. 20, No. 9, 10.2017, p. A406-A407.

Research output: Contribution to journal › Meeting Abstract › peer-review

TY - JOUR

T1 - Does using factor analysis to select covariates in mapping algorithms improve predictive accuracy? A case of mapping the core 23-item Women’s Health Questionnaire onto the EQ-5D-5L and SF-6D

AU - Kaambwa, B.

AU - de Lacey, S.

AU - Smith, C.

AU - Ratcliffe, J.

PY - 2017/10

Y1 - 2017/10

N2 - Objectives: In addition to theoretical justifications, many statistical methods have been used for selecting covariates to include in algorithms mapping non-utility 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. 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: EuroQoL 5 Dimensions 5 Level (EQ-5D-5L) and the Short Form 6 Dimensions (SF-6D).

AB - Objectives: In addition to theoretical justifications, many statistical methods have been used for selecting covariates to include in algorithms mapping non-utility 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. 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: EuroQoL 5 Dimensions 5 Level (EQ-5D-5L) and the Short Form 6 Dimensions (SF-6D).

KW - EQ-5D-5L

KW - Factor analysis

KW - Mapping

KW - SF-6D

KW - Utilities

KW - WHQ-23

U2 - 10.1016/j.jval.2017.08.052

DO - 10.1016/j.jval.2017.08.052

M3 - Meeting Abstract

SN - 1098-3015

VL - 20

SP - A406-A407

JO - Value in Health

JF - Value in Health

IS - 9

T2 - ISPOR 20th Annual European Congress

Y2 - 4 November 2017 through 8 November 2017

ER -

Does using factor analysis to select covariates in mapping algorithms improve predictive accuracy? A case of mapping the core 23-item Women’s Health Questionnaire onto the EQ-5D-5L and SF-6D

Abstract

Keywords

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