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Simulation study reveals superior calibration of random appraisal-weighted meta-analysis for heterogeneous clinical datasets, indicating robust quantitative benchmarking under EU MDR.
Background The European Union Medical Device Regulation (EU MDR) requires manufacturers to establish quantitative, state of the art parameters for clinical evaluation. Because most available evidence is heterogeneous and uncontrolled, conventional meta-analytic methods provide limited support for deriving device level benchmarks. Methods A methodological framework was developed using synthetic datasets reflecting typical real-world evidence. Study means, variances and standard errors were combined using fixed effect, DerSimonian–Laird random effects, an Appraisal-Weighted (AW) approach, and a Random Appraisal-Weighted (RAW) method. AW incorporates structured methodological appraisal into random-effects weights, while RAW retains the appraisal-weighted point estimate but uses random-effects confidence intervals. Separate pooled estimates and 95% confidence intervals were generated for state of the art and device specific datasets and interpreted using predefined interpretive performance zones. Results Fixed effect pooling produced overly narrow intervals dominated by a single large, lower quality study. Random effects estimation widened intervals but did not address methodological quality. Appraisal-weighting improved point-estimate accuracy when appraisal was informative of bias but produced overly confident confidence intervals. The RAW approach preserved the accuracy gains of appraisal-weighting while maintaining appropriate interval calibration. Comparison of state of the art and device specific RAW intervals yielded intuitive classification of performance. Conclusions The RAW method offers a transparent, reproducible approach for transforming heterogeneous clinical evidence into quantitative benchmarks suitable for benefit–risk assessment under EU MDR. The framework aligns with regulatory expectations, accommodates evolving PMCF evidence, and supports structured, defensible clinical evaluation.
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George Miller (2026) studied this question.
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