Presentation (CTI)

OBJECTIVES: Meta-analyses of continuous outcomes often face challenges when pooling data from studies using different measurement instruments (e.g., depressive symptoms assessed via Beck Depression Inventory versus Hamilton Depression Rating Scale). To address the challenge of synthesizing heterogeneous effect estimates, standardization methods—such as calculatingstandardized mean differences (SMDs)—are commonly employed. While pooled SMDs with 95% confidence intervals (CIs) indicate statisticalsignificance when excluding zero, this approach does not convey whether the effects are clinically meaningful. For healthcare decision-makers, assessing clinical relevance against established thresholds, such as the Minimal Clinically Important Difference (MCID), is essential for interpreting the real-world impact of interventions. To develop and demonstrate a methodological framework that enhances the clinical interpretability of meta-analytic results by linkingstandardized treatment effects to MCID-based thresholds
METHODS: Building on conventional standardization approaches, we propose an analytic strategy that estimates the probability that an observed treatment effect meets or exceeds a clinically meaningful threshold. By incorporating the MCID of a target scale and modeling the distribution of pooled effects, the method translates SMDs into interpretable metrics for clinical and policy decision-making. This approach is adaptable to different populations and outcomes, andamenable to automation within evidence synthesis workflows
RESULTS: A Cochrane review reported a SMD of -0.22 (95% CI -0.33 to -0.10) for cognitive behavioral therapy compared to treatment as usual in chronic non-cancer pain patients. Applying our novel methodological framework, despite statisticalsignificance, the probability of clinical significance for this therapy ranged between 0.1% to 0.8% using different pain scales
CONCLUSIONS: The proposed framework provides a probabilistic interpretation of clinical relevance, complementing traditional statistical outputs. By quantifying the likelihood that an intervention yields a meaningful benefit, this method enhances the utility of meta-analyses for HTA, guideline development, and value assessment, supporting more transparent, patient-centered, and actionable healthcare decisions