In the era of precision medicine, developing accurate predictive models for cancer prognosis can guide clinical treatments and improve patient survival. Multi-omics integration can improve the performance and interpretability of prognostic models. However, multi-omics integration models have limitations owing to the heterogeneity of data and the complex regulatory relationships among different platforms. This study proposes a novel Bayesian Cox proportional hazards model with a structural equation model framework (BSEMsurvCox) to integrate three omics platforms. The No U-turn sampling (NUTS) algorithm was used to fit our model. Extensive simulation studies have shown that our model is superior to two non-integrated models including Bayesian Cox and Traditional Cox and an integration model named Block Forest Cox. Furthermore, two real datasets were used to demonstrate the superiority of the proposed model. The study findings indicate that the BSEMsurvCox model provides a higher predictive performance and biological interpretability than non-integrated and other integration models.
Wang et al. (Thu,) studied this question.