Evidence map›Paper›PMID 22984825›Full record

ArticleBMC medical research methodology2012

Longitudinal beta regression models for analyzing health-related quality of life scores over time.

Matthias Hunger, Angela Döring, Rolf Holle

Abstract read
In one paragraph

Article in BMC medical research methodology, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers.

0numbers the graph read from it
0cells of the map it votes in
45citing 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

45 citing papers in PubMed.

  1. Trial
  2. Trial
  3. Trial
  4. Article
  5. Article
  6. Predicting EQ-5D-3L utility values from clinical data in a prospective cohort of kidney transplant recipients.The European journal of health economics : HEPAC : health economics in prevention and care · 2026
    Observational
  7. Article
  8. Article
  9. Article
  10. Socio-demographic disparities in health-related quality of life in hypertensive patients in Bangladesh: a comprehensive survey analysis.Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2025
    Article
  11. Observational
  12. Article
  13. Analyzing quality of life among people with opioid use disorder from the National Institute on Drug Abuse Data Share initiative: implications for decision making.Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2024
    Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. Mapping the Peds QLFrontiers in public health · 2023
    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

3 authors.

Matthias HungerHelmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Institute of Health Economics and Health Care Management, Neuherberg, 85764, Germany. matthias.hunger@helmholtz-muenchen.de
Angela Döring
Rolf Holle

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundHealth-related quality of life (HRQL) has become an increasingly important outcome parameter in clinical trials and epidemiological research. HRQL scores are typically bounded at both ends of the scale and often highly skewed. Several regression techniques have been proposed to model such data in cross-sectional studies, however, methods applicable in longitudinal research are less well researched. This study examined the use of beta regression models for analyzing longitudinal HRQL data using two empirical examples with distributional features typically encountered in practice.

methodsWe used SF-6D utility data from a German older age cohort study and stroke-specific HRQL data from a randomized controlled trial. We described the conceptual differences between mixed and marginal beta regression models and compared both models to the commonly used linear mixed model in terms of overall fit and predictive accuracy.

resultsAt any measurement time, the beta distribution fitted the SF-6D utility data and stroke-specific HRQL data better than the normal distribution. The mixed beta model showed better likelihood-based fit statistics than the linear mixed model and respected the boundedness of the outcome variable. However, it tended to underestimate the true mean at the upper part of the distribution. Adjusted group means from marginal beta model and linear mixed model were nearly identical but differences could be observed with respect to standard errors.

conclusionsUnderstanding the conceptual differences between mixed and marginal beta regression models is important for their proper use in the analysis of longitudinal HRQL data. Beta regression fits the typical distribution of HRQL data better than linear mixed models, however, if focus is on estimating group mean scores rather than making individual predictions, the two methods might not differ substantially.

Indexed as

Health StatusQuality of LifeAgedDiabetes MellitusFemaleHealthHumansLinear ModelsLongitudinal StudiesMaleMiddle AgedRegression AnalysisStrokeSurveys and Questionnaires

Identifiers

PMID22984825
PMCPMC3528618

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.