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Patient-reported outcomes in meta-analyses-part 2: methods for improving interpretability for decision-makers.
Bradley C Johnston1, Donald L Patrick, Kristian Thorlund
1Department of Anesthesia and Pain Medicine, and Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada. bradley.johnston@sickkids.ca.
Health and Quality of Life Outcomes
|December 24, 2013
Summary
This study offers methods to improve understanding of patient-reported outcomes (PROs) in meta-analyses. Presenting results in natural units or minimal important difference units enhances decision-making for healthcare choices.
Area of Science:
- Medical Informatics
- Biostatistics
- Health Services Research
Background:
- Systematic reviews and meta-analyses incorporating patient-reported outcomes (PROs) are vital for healthcare decisions.
- Interpreting the magnitude of effect for PROs is challenging due to diverse measurement instruments across trials.
Purpose of the Study:
- To summarize approaches for enhancing the interpretability of pooled PRO estimates in meta-analyses.
- To guide systematic review authors in presenting PRO data effectively.
Main Methods:
- Recommends using standardized mean difference alongside other methods for presenting PROs.
- Suggests presenting dichotomized treatment effects (relative risk, odds ratio, risk difference).
- Advocates for presenting results in natural units, minimal important difference units, or ratio of means.
Main Results:
- Pooled estimates of PROs can be presented in various formats to improve clarity.
- Different presentation methods offer complementary insights into treatment effects.
- The chosen method should facilitate interpretation of the magnitude of the treatment effect.
Conclusions:
- Enhanced interpretability of PRO meta-analyses supports better-informed patient, clinician, and policy-maker decisions.
- Utilizing diverse presentation formats aids in understanding the clinical significance of PRO findings.
- Improved PRO meta-analysis methods contribute to more effective healthcare decision-making.
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