Bayesian Survival Model Based on Moment Characterization

In this paper, we study the performance of Bayesian computational methods to estimate the parameters of a bivariate survival model based on the Ali–Mikhail–Haq copula with marginal distributions given by Weibull distributions. The estimation procedure was based on Monte Carlo Markov Chain (MCMC) algorithms. We present three version of the Metropolis–Hastings algorithm: Independent Metropolis–Hastings (IMH), Random Walk Metropolis (RWM) and Metropolis–Hastings with a natural-candidate generating density (MH). Since the creation of a good candidate generating density in IMH and RWM may be difficult, we also describe how to update a parameter of interest using the slice sampling (SS) method. A simulation study was carried out to compare the performances of the IMH, RWM and SS. A comparison was made using the sample root mean square error as an indicator of performance. Results obtained from the simulations show that the SS algorithm is an effective alternative to the IMH and RWM methods when simulating values from the posterior distribution, especially for small sample sizes. We also applied these methods to a real data set.

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Development of a Survival Model Based on Autophagy-Associated Genes for Predicting Prognosis of Gastric Cancer

10.21203/rs.3.rs-30248/v1 ◽

2020 ◽

Author(s):

Wanli Yang ◽

Lili Duan ◽

Xinhui Zhao ◽

Liaoran Niu ◽

Yiding Li ◽

...

Keyword(s):

Gastric Cancer ◽

Regression Analysis ◽

Prediction Model ◽

Roc Curve ◽

Survival Model ◽

Differentially Expressed ◽

Lasso Regression ◽

Prognosis Prediction ◽

Cellular Processes ◽

Model Based

Abstract Background: Gastric cancer (GC) is one of lethal diseases worldwide. Autophagy-associated genes play a crucial role in the cellular processes of GC. Our study aimed to investigate and identify the prognostic potential of autophagy-associated genes signature in GC. Methods: RNA-seq and clinical information of GC and normal controls were downloaded from The Cancer Genome Atlas (TCGA) database. Then, the Wilcoxon signed-rank test was used to pick out the differentially expressed autophagy-associated genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed to investigate the potential roles and mechanisms of autophagy-associated genes in GC. Cox proportional hazard regression analysis and Lasso regression analysis were carried out to identify the overall survival (OS) related autophagy-associated genes, which were then collected to construct a predictive model. Kaplan-Meier method and receiver operating characteristic (ROC) curve were utilized to validate the accuracy of this model. Finally, a clinical nomogram was established by combining the clinical factors and autophagy-associated genes signature. Results: A total of 28 differentially expressed autophagy-associated genes were identified. GO and KEGG analyses revealed that several important cellular processes and signaling pathways were correlated with these genes. Through Cox regression and Lasso regression analyses, we identified 4 OS-related autophagy-associated genes (GRID2, ATG4D, GABARAPL2, and CXCR4) and constructed a prognosis prediction model. GC Patients with high-risk had a worse OS than those in low-risk group (5-year OS, 27.7% vs 38.3%; P=9.524e-07). The area under the ROC curve (AUC) of the prediction model was 0.67. The nomogram was demonstrated to perform better for predicting 3-year and 5-year survival possibility for GC patients with a concordance index (C-index) of 0.70 (95% CI: 0.65-0.72). The calibration curves also presented good concordance between nomogram-predicted survival and actual survival. Conclusions: We constructed and evaluated a survival model based on the autophagy-associated genes for GC patients, which may improve the prognosis prediction in GC.

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