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PharmacoEconomics

, Volume 21, Issue 3, pp 191–200 | Cite as

A Meta-Analysis of Quality-of-Life Estimates for Stroke

  • Tammy O. TengsEmail author
  • Ting H. Lin
Original Research Article

Abstract

Background: Researchers performing cost-effectiveness analyses often incorporate quality-of-life (QOL) estimates.

Objective: To aid analysts, we performed a meta-analysis to estimate quality of life for minor, moderate, and major stroke and assessed the relative importance of study design characteristics in predicting the quality of life of patients with stroke.

Methods: Through a systematic search we identified 20 articles reporting 53 unique QOL weights for stroke. Each article was read and QOL weights and study characteristics were recorded. We used a hierarchical linear model (HLM) to perform a meta-regression. The model included severity of stroke, elicitation method, respondents, and QOL scale bounds as explanatory variables.

Results: Severity of stroke (p < 0.0001) and the bounds of the scale (p = 0.0015) were significant predictors of quality of life, while the elicitation method and respondents were not. Pooling QOL weights using the HLM model, we estimated a quality of life of 0.52 for major stroke, 0.68 for moderate stroke, and 0.87 for minor stroke if the time trade-off method is used to assess quality of life from community members when the scale bounds range from death to perfect health.

Conclusions: We found no systematic difference in stroke QOL weights depending on elicitation method or respondents. However, quality of life is sensitive to the bounds of the scale. Because the pooled QOL estimates reported here are based on a comprehensive review of the QOL literature for stroke, they should be of great use to researchers performing cost-utility analyses of interventions designed to prevent or treat stroke, or where stroke is a possible side effect of therapy.

Keywords

Hierarchical Linear Model Standard Gamble Perfect Health Health Utility Index Minor Stroke 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

Michelle Yu and Elvina Luistro helped with earlier efforts to code quality of life estimates related to stroke and we appreciate their assistance. This research was funded in part by the California Tobacco-Related Disease Research Program (Grant Number 6PT-3005). The authors have no conflicts of interest that are directly relevant to the contents of this manuscript.

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Copyright information

© Adis International Limited 2003

Authors and Affiliations

  1. 1.Health Priorities Research Group, School of Social EcologyUniversity of CaliforniaIrvineUSA

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