Externally anchored covariate completion improved target-population effect estimation in meta-analysis with...

To develop and evaluate externally anchored covariate completion for meta-analysis (EACC-Meta), a framework that enables target-population treatment effect estimation from incomplete aggregate data by borrowing covariance structure from external real-world data, without requiring individual participant data.

We conducted a Monte Carlo simulation study of target-population meta-analysis under incomplete aggregate data. Binary outcomes were generated under a probit model with four binary covariates, including two effect modifiers. We compared EACC-Meta with conventional fixed-effect and random-effects meta-analysis across transport intensities, meta-analysis sizes, large-trial proportions, and covariate omission scenarios.

EACC-Meta consistently achieved lower bias and root mean squared error than conventional meta-analysis, with the advantage increasing as transport shift grew. Under high transport, conventional methods exhibited near-zero coverage probability, whereas EACC-Meta achieved higher coverage when key effect modifiers were included, although coverage remained below nominal under moderate-to-high transport. Omitting true effect modifiers attenuated the advantage to conventional levels. Results were robust across external data pools.

EACC-Meta effectively improves the missing data limitations inherent in aggregate data evidence synthesis. By rigorously integrating external structural information, it provides a transparent and potentially useful pathway for estimating treatment effects in nontrial target populations without requiring IPD.

When doctors or health agencies want to know whether a treatment works for a specific group of patients, such as older adults with diabetes, they typically rely on combining results from multiple clinical trials, a process called meta-analysis. However, published trial reports rarely provide the detailed patient-level data needed to tailor these estimates to a particular population. This creates a gap between what the evidence shows for the average trial participant and what is needed for a real-world patient group. We developed a new method called EACC-Meta to bridge this gap. Rather than using outside data to replace trial results, our method uses outside databases only to learn how patient characteristics tend to occur together. This helps fill in missing information from published trial summaries and estimate how well a treatment is likely to work in the population of interest. In a large simulation study with nearly 2000 test scenarios, EACC-Meta produced more accurate point estimates than standard meta-analysis, especially when the target population differed substantially from the trial populations, although the uncertainty around these estimates may remain imperfect in such cases. The method performed well as long as the key factors that influence treatment response were reported in the trial publications. EACC-Meta offers a practical tool for evidence-based decision-making when individual patient data are unavailable and may help treatment recommendations better reflect the needs of specific patient groups.