PMID- 34452708
OWN - NLM
STAT- MEDLINE
DCOM- 20211230
LR  - 20211230
IS  - 1524-4733 (Electronic)
IS  - 1098-3015 (Linking)
VI  - 24
IP  - 9
DP  - 2021 Sep
TI  - Mapping EQ-5D-3L to EQ-5D-5L.
PG  - 1285-1293
LID - S1098-3015(21)00170-4 [pii]
LID - 10.1016/j.jval.2021.03.009 [doi]
AB  - OBJECTIVES: The original 3-level EQ-5D (EQ-5D-3L) includes 5 dimensions with 3 
      levels of problems per dimension. Since 2010, a more sensitive version with 5 
      levels of problems per dimension (EQ-5D-5L) has become available. Population 
      value sets have been developed for both versions of the questionnaire. The 
      objective of this research was to develop a mapping function to link EQ-5D-3L 
      responses to value sets for the EQ-5D-5L. METHODS: Various algorithms were 
      developed to link EQ-5D-3L and EQ-5D-5L responses using data from an 
      observational study including members of 10 subgroups (N = 3580) who completed 
      both versions of the questionnaire. Nonparametric and ordinal logistic regression 
      models were fit to the data and compared using Akaike's information criterion 
      (AIC) as well as the mean absolute error and root mean squared error of 
      predictions. Results were contrasted qualitatively and quantitatively with those 
      of an alternative copula-based approach. RESULTS: Including indicants of problems 
      for other EQ-5D-3L dimensions as regressors in the modeling yielded the greatest 
      improvement in prediction accuracy. Adding age and gender lowered the AIC without 
      improving predictions, while including a latent factor lowered the AIC further 
      and slightly improved predictive accuracy. Models that conditioned on problems in 
      other EQ-5D-3L dimensions yielded more accurate predictions than the alternative 
      copula-based approach in subgroups defined by age and gender. CONCLUSION: We 
      present novel algorithms to map EQ-5D-3L responses to EQ-5D-5L value sets. The 
      recommended approach is based on an ordinal logistic regression that disregards 
      age and gender and accounts for unobserved heterogeneity using a latent factor.
CI  - Copyright (c) 2021 ISPOR-The Professional Society for Health Economics and Outcomes 
      Research. Published by Elsevier Inc. All rights reserved.
FAU - van Hout, Ben A
AU  - van Hout BA
AD  - University of Sheffield. Electronic address: b.a.vanhout@sheffield.ac.uk.
FAU - Shaw, James W
AU  - Shaw JW
AD  - Bristol Meyers Squibb, Princeton Pike, Lawrenceville, NJ.
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20210518
PL  - United States
TA  - Value Health
JT  - Value in health : the journal of the International Society for Pharmacoeconomics 
      and Outcomes Research
JID - 100883818
SB  - IM
MH  - Algorithms
MH  - *Health Status
MH  - *Health Surveys
MH  - Logistic Models
OTO - NOTNLM
OT  - EQ-5D-3L
OT  - EQ-5D-5L
OT  - algorithms
OT  - mapping
EDAT- 2021/08/29 06:00
MHDA- 2021/12/31 06:00
CRDT- 2021/08/28 05:24
PHST- 2020/05/19 00:00 [received]
PHST- 2021/02/05 00:00 [revised]
PHST- 2021/03/02 00:00 [accepted]
PHST- 2021/08/28 05:24 [entrez]
PHST- 2021/08/29 06:00 [pubmed]
PHST- 2021/12/31 06:00 [medline]
AID - S1098-3015(21)00170-4 [pii]
AID - 10.1016/j.jval.2021.03.009 [doi]
PST - ppublish
SO  - Value Health. 2021 Sep;24(9):1285-1293. doi: 10.1016/j.jval.2021.03.009. Epub 
      2021 May 18.