Tammy O Tengs1, Ting H Lin. 1. Health Priorities Research Group, School of Social Ecology, University of California, Irvine, California 92697-7076, USA. tengs@uci.edu
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.
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.
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