ArticleAsian Pacific journal of cancer prevention : APJCP2018
Comparison of Survival Models for Analyzing Prognostic Factors in Gastric Cancer Patients
Article in Asian Pacific journal of cancer prevention : APJCP, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- A large-scale neutral comparison study of survival models on low-dimensional data.Bioinformatics (Oxford, England) · 2026Article
- Feature Selection and Model Optimization for Survival Prediction in Patients with Angina Pectoris.Journal of clinical medicine · 2025Article
- Cumulative Systolic Blood Pressure and Incident Stroke Type Variation by Race and Ethnicity.JAMA network open · 2024Article
- Comparing parametric and Cox regression models using HIV/AIDS survival data from a retrospective study in Ntcheu district in Malawi.Journal of public health research · 2022Article
- Expression of miR-141 and YAP1 in gastric carcinoma and modulation of cancer cell proliferation and apoptosis.International journal of clinical and experimental pathology · 2019Article
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Authors and funding
5 authors.
Funding
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Abstract
Objective: There are a number of models for determining risk factors for survival of patients with gastric cancer. This study was conducted to select the model showing the best fit with available data. Methods: Cox regression and parametric models (Exponential, Weibull, Gompertz, Log normal, Log logistic and Generalized Gamma) were utilized in unadjusted and adjusted forms to detect factors influencing mortality of patients. Comparisons were made with Akaike Information Criterion (AIC) by using STATA 13 and R 3.1.3 softwares. Results: The results of this study indicated that all parametric models outperform the Cox regression model. The Log normal, Log logistic and Generalized Gamma provided the best performance in terms of AIC values (179.2, 179.4 and 181.1, respectively). On unadjusted analysis, the results of the Cox regression and parametric models indicated stage, grade, largest diameter of metastatic nest, largest diameter of LM, number of involved lymph nodes and the largest ratio of metastatic nests to lymph nodes, to be variables influencing the survival of patients with gastric cancer. On adjusted analysis, according to the best model (log normal), grade was found as the significant variable. Conclusion: The results suggested that all parametric models outperform the Cox model. The log normal model provides the best fit and is a good substitute for Cox regression.
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