C-STREAM: A new meta-analysis engine for Transportability

Abstract

Abstract

C-STREAM, is a  repurposed methodology which uses causal transport theory to standardize historical trials to a single target demographic. Validation shows that on complex simulated datasets a 60-80% reduction in unexplained heterogeneity is possible , revealing consistent efficacy.

Paper Summary

Introduction Evidence synthesis issues stem from  standard meta-analysis and its inability to distinguish between true biological failure and demographic effects . This problem often leads to "conflicting evidence" thatmay be an  artifact of population variance.

Methodology C-STREAM solves this by shifting the unit of analysis from raw effect sizes to useful and well thought through counterfactual models. The engine employs three core innovations:

1. Counterfactual Transport: g-computation to mathematically re-weight historical clinical trials, predicting treatment and their performance in a specific target patient population.
2. Variance Decomposition: "Structural Heterogeneity" (true mechanical differences) is split from from "Transport Heterogeneity" (population mismatch).
3. Transport-Aware VOI: This is validated but repurposed and generates Net Benefit curves to calculate the exact optimal sample size for future trials based on residual uncertainty.

Results Tested on  simulations featuring high confounding and structural outliers, C-STREAM successfully resolved population mismatches. The approach achieved a 60-80% reduction in unexplained heterogeneity but needs further modeling. 

Conclusion C-STREAM offers a new method toward more precision meta-analysis. By isolating biological efficacy  (as opposed) to demographic noise.