Evidence map›Paper›PMID 32188480›Full record

ArticleHealth and quality of life outcomes2020

Assessing the proxy response bias of EQ-5D-3 L in general population: a study based on a large-scale representative household health survey using propensity score matching.

Ying Liang, Tianle Che, Haiyue Zhang, Lei Shang, Yuhai Zhang, Yongyong Xu, Lingxia Guo, Zhijun Tan

Abstract read
In one paragraph

Article in Health and quality of life outcomes, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

  1. Article
  2. Observational
  3. Examining health-related quality of life in ambulatory adult patients with chronic heart failure: insights from Malaysia using EQ-5D-5L.Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2024
    Article
  4. Observational
  5. Article
  6. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Ying LiangDepartment of Health Statistics, Fourth Military Medical University, Xi'an, Shaanxi Province, China.
Tianle CheDepartment of Health Statistics, Fourth Military Medical University, Xi'an, Shaanxi Province, China.
Haiyue ZhangDepartment of Health Statistics, Fourth Military Medical University, Xi'an, Shaanxi Province, China.
Lei ShangDepartment of Health Statistics, Fourth Military Medical University, Xi'an, Shaanxi Province, China.
Yuhai ZhangDepartment of Health Statistics, Fourth Military Medical University, Xi'an, Shaanxi Province, China.
Yongyong XuDepartment of Health Statistics, Fourth Military Medical University, Xi'an, Shaanxi Province, China.
Lingxia GuoCenter of Health Statistics, Health General Office of Shaanxi Province, Xi'an, Shaanxi Province, China.
Zhijun TanDepartment of Health Statistics, Fourth Military Medical University, Xi'an, Shaanxi Province, China. tanzjfmmu@126.com.

Funding

Key R & D plan of Shaanxi Province 2019SF-143National Natural Science Foundations 81573250National Natural Science Foundations 81573252National Natural Science Foundations 81703315National Natural Science Foundations 81773540
6 · The paper itself

Abstract

backgroundProxy respondent-someone who assists the intended respondent or responds on their behalf-are widely applied in the measurement of health-related quality of life (HRQL). However, proxies may not provide the same responses as the intended respondents, which may bias the findings.

objectivesTo determine whether the use of proxies is related to socio-demographic characteristics of the intended respondent, and to assess the possible proxy response bias of Chinese version of EQ-5D-3 L in general population.

methodsA cross-sectional study based on a provincially representative sample from 2013 National Health Service Survey (NHSS) in Shaanxi, China was performed. HRQL was measured by Chinese version of EQ-5D-3 L. Propensity score matching (PSM) was used to get matched pairs of self-reports and proxy-reports. Before and after PSM, univariate logistic and linear models including the indicator of proxy response as the only independent variable, were employed to assess the possible proxy response bias of the dimensional and overall health status of EQ-5D-3 L respectively.

results19.9% of the responses involved a proxy. Before PSM, the proxy-report group was younger in age and reported less unhealthy lifestyle, lower prevalence of disease, and less hospitalization than the self-report group. After PSM, it showed that the proxy-report group was statistically more likely to report health problem on each dimension of EQ-5D-3 L, with odds ratios larger than one comparing with self-report group. The means of EQ-5D-3 L index and EQ VAS of proxy-report group were 0.022 and 0.834 lower than self-report group.

conclusionsSignificantly negative proxy response bias was found in Chinese EQ-5D-3 L in general population, and the magnitude of the bias was larger in physical dimensions than psychological dimensions after using PSM to control confounders.

Indexed as

Health StatusQuality of LifeAdultAgedChinaCross-Sectional StudiesFemaleHealth SurveysHumansMaleMiddle AgedPropensity ScoreProxySelf ReportSocioeconomic FactorsYoung AdultEQ-5D-3 LHealth-related quality of lifePropensity score matchingProxy response bias

Identifiers

PMID32188480
PMCPMC7079393

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.