BACKGROUND

In multi-site studies in clinical practice, the treatment effects may exhibit potential heterogeneity across different sites. Additionally, propensity score methods used to adjust for confounding may suffer from model misspecification, which can lead to biased estimates. Addressing both the potential heterogeneity of site-specific treatment effects and the issues of model misspecification in the context of multi-site survival data is a critical area that warrants further research.

METHODS

We propose a novel partially heterogeneous weighted fusion learning method. This approach is designed to simultaneously identify and estimate potential heterogeneous treatment effects across sites, while also improving robustness against model misspecification in the estimation of survival causal effects across different sites. We evaluate the performance of this method through simulation studies and apply it to the real-world data from the Surveillance, Epidemiology, and End Result (SEER) database to assess whether the survival effect of surgery with adjuvant radiation therapy for breast cancer patients differs across sites in a multi-site study.

RESULTS

Simulation studies demonstrate that our proposed method accurately identifies the potential heterogeneous treatment effects, and when the candidate models include the correct model, our method performs comparably to methods based on correctly specified propensity score models in estimating the site-specific hazard ratios. The application of our method to the SEER database revealed two distinct survival effects of surgery with adjuvant radiation therapy for breast cancer patients across sites. However, compared to our method, traditional methods that pool all sites together failed to identify this heterogeneity, while analyzing each site individually led to a reduction in statistical power.

CONCLUSION

This study introduces a partially heterogeneous weighted fusion learning method for survival data that effectively identifies potential inter-site heterogeneity in treatment effects, while simultaneously addressing the issue of model misspecification.