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In evidence synthesis, collecting individual participant data (IPD) across eligible studies is the most reliable way to investigate the treatment effects in different subgroups defined by participant characteristics. Nonetheless, access to all IPD from all studies might be very challenging due to privacy concerns. To overcome this, many approaches such as multilevel modeling have been proposed to incorporate the vast amount of aggregated data from the literature into IPD meta-analysis. These methods, however, often rely on specifying separate models for trial-level versus patient-level data, which likely suffers from ecological bias when there are non-linearities in the outcome generating mechanism. In this paper, we introduce a novel method to combine aggregated data and IPD in meta-analysis that is free from ecological bias. The proposed approach relies on modeling the study membership given covariates, then using inverse weighting to estimate the trial-specific coefficients in the individual-level outcome model of studies without IPD accessible. The weights derived from this approach also shed insights on the similarity in the case-mix across studies, which is useful to assess whether eligible trials are sufficiently similar to be meta-analyzed. We evaluate the proposed method by synthetic data, then apply it to a real-world meta-analysis comparing the chance of response between guselkumab and adalimumab among patients with psoriasis.

The presence of intermediate confounders, also called recanting witnesses, is a fundamental challenge to the investigation of causal mechanisms in mediation analysis, preventing the identification of natural path-specific effects. Proposed alternative parameters (such as randomizational interventional effects) are problematic because they can be non-null even when there is no mediation for any individual in the population; i.e., they are not an average of underlying individual-level mechanisms. In this paper we develop a novel method for mediation analysis in settings with intermediate confounding, with guarantees that the causal parameters are summaries of the individual-level mechanisms of interest. The method is based on recently proposed ideas that view causality as the transfer of information, and thus replace recanting witnesses by draws from their conditional distribution, what we call "recanting twins". We show that, in the absence of intermediate confounding, recanting twin effects recover natural path-specific effects. We present the assumptions required for identification of recanting twins effects under a standard structural causal model, as well as the assumptions under which the recanting twin identification formulas can be interpreted in the context of the recently proposed separable effects models. To estimate recanting-twin effects, we develop efficient semi-parametric estimators that allow the use of data driven methods in the estimation of the nuisance parameters. We present numerical studies of the methods using synthetic data, as well as an application to evaluate the role of new-onset anxiety and depressive disorder in explaining the relationship between gabapentin/pregabalin prescription and incident opioid use disorder among Medicaid beneficiaries with chronic pain.