arXiv subjects

Renato Panaro

Publications and source records attributed to Renato Panaro.

3 recordsLinked to original sources

arXiv2026

Empirical prior distributions for treatment-by-subgroup interaction heterogeneity in random-effects meta-analysis

Subgroup analyses are central to the assessment of benefits and risks, where recommendations may depend on evidence that treatment effects differ across patient groups. Valid subgroup claims require evidence based on (within-trial) interaction estimates while accounting for the heterogeneity in those interaction effects. In the common case of only a few available studies, inference may benefit from the use of prior information on the expected amount of heterogeneity. Although between-study heterogeneity~($\tau$) has been studied empirically for overall treatment effects, no such calibration exists for treatment-by-subgroup interaction effects. We derive empirical (predictive) prior distributions for overall and interaction effect heterogeneity from over 3{,}000 interaction meta-analyses drawn from the \emph{Cochrane Database of Systematic Reviews (CDSR)}. The resulting effect-measure-specific priors indicate that interaction heterogeneity tends to be substantially smaller than treatment effect heterogeneity. We also show that lower precision of within-trial interaction estimates makes interaction heterogeneity harder to identify. Therefore, the use of empirical priors is particularly valuable in sparse interaction meta-analyses. A motivating example illustrates how priors tailored to interaction effects may substantially improve precision in a meta-analysis compared with standard heterogeneity priors.

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arXiv2025

Consistent Bayesian meta-analysis on subgroup specific effects and interactions

Commonly, clinical trials report effects not only for the full study population but also for patient subgroups. Meta-analyses of subgroup-specific effects and treatment-by-subgroup interactions may be inconsistent, especially when trials apply different subgroup weightings. We show that meta-regression can, in principle, with a contribution adjustment, recover the same interaction inference regardless of whether interaction data or subgroup data are used. Our Bayesian framework for subgroup-data interaction meta-analysis inherently (i) adjusts for varying relative subgroup contribution, quantified by the information fraction (IF) within a trial; (ii) is robust to prevalence imbalance and variation; (iii) provides a self-contained, model-based approach; and (iv) can be used to incorporate prior information into interaction meta-analyses with few studies.The method is demonstrated using an example with as few as seven trials of disease-modifying therapies in relapsing-remitting multiple sclerosis. The Bayesian Contribution-adjusted Meta-analysis by Subgroup (CAMS) indicates a stronger treatment-by-disability interaction (relapse rate reduction) in patients with lower disability (EDSS <= 3.5) compared with the unadjusted model, while results for younger patients (age < 40 years) are unchanged.By controlling subgroup contribution while retaining subgroup interpretability, this approach enables reliable interaction decision-making when published subgroup data are available.Although the proposed CAMS approach is presented in a Bayesian context, it can also be implemented in frequentist or likelihood frameworks.

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arXiv2025

Subgroup comparisons within and across studies in meta-analysis

Subgroup-specific meta-analysis synthesizes treatment effects for patient subgroups across randomized trials. Methods include joint or separate modeling of subgroup effects and treatment-by-subgroup interactions, but inconsistencies arise when subgroup prevalence differs between studies (e.g., proportion of non-smokers). A key distinction is between study-generated evidence within trials and synthesis-generated evidence obtained by contrasting results across trials. This distinction matters for identifying which subgroups benefit or are harmed most. Failing to separate these evidence types can bias estimates and obscure true subgroup-specific effects, leading to misleading conclusions about relative efficacy. Standard approaches often suffer from such inconsistencies, motivating alternatives. We investigate standard and novel estimators of subgroup and interaction effects in random-effects meta-analysis and study their properties. We show that using the same weights across different analyses (SWADA) resolves inconsistencies from unbalanced subgroup distributions and improves subgroup and interaction estimates. Analytical and simulation studies demonstrate that SWADA reduces bias and improves coverage, especially under pronounced imbalance. To illustrate, we revisit recent meta-analyses of randomized trials of COVID-19 therapies. Beyond COVID-19, the findings outline a general strategy for correcting compositional bias in evidence synthesis, with implications for decision-making and statistical modeling. We recommend the Interaction RE-weights SWADA as a practical default when aggregation bias is plausible: it ensures collapsibility, maintains nominal coverage with modest width penalty, and yields BLUE properties for the interaction.

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