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Li X, Marcus D, Russell J, Aboagye EO, Ellis LB, Sheeka A, Park WHE, Bharwani N, Ghaem-Maghami S, Rockall AG. Weibull parametric model for survival analysis in women with endometrial cancer using clinical and T2-weighted MRI radiomic features. BMC Med Res Methodol 2024; 24:107. [PMID: 38724889 PMCID: PMC11080307 DOI: 10.1186/s12874-024-02234-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Accepted: 04/23/2024] [Indexed: 05/13/2024] Open
Abstract
BACKGROUND Semiparametric survival analysis such as the Cox proportional hazards (CPH) regression model is commonly employed in endometrial cancer (EC) study. Although this method does not need to know the baseline hazard function, it cannot estimate event time ratio (ETR) which measures relative increase or decrease in survival time. To estimate ETR, the Weibull parametric model needs to be applied. The objective of this study is to develop and evaluate the Weibull parametric model for EC patients' survival analysis. METHODS Training (n = 411) and testing (n = 80) datasets from EC patients were retrospectively collected to investigate this problem. To determine the optimal CPH model from the training dataset, a bi-level model selection with minimax concave penalty was applied to select clinical and radiomic features which were obtained from T2-weighted MRI images. After the CPH model was built, model diagnostic was carried out to evaluate the proportional hazard assumption with Schoenfeld test. Survival data were fitted into a Weibull model and hazard ratio (HR) and ETR were calculated from the model. Brier score and time-dependent area under the receiver operating characteristic curve (AUC) were compared between CPH and Weibull models. Goodness of the fit was measured with Kolmogorov-Smirnov (KS) statistic. RESULTS Although the proportional hazard assumption holds for fitting EC survival data, the linearity of the model assumption is suspicious as there are trends in the age and cancer grade predictors. The result also showed that there was a significant relation between the EC survival data and the Weibull distribution. Finally, it showed that Weibull model has a larger AUC value than CPH model in general, and it also has smaller Brier score value for EC survival prediction using both training and testing datasets, suggesting that it is more accurate to use the Weibull model for EC survival analysis. CONCLUSIONS The Weibull parametric model for EC survival analysis allows simultaneous characterization of the treatment effect in terms of the hazard ratio and the event time ratio (ETR), which is likely to be better understood. This method can be extended to study progression free survival and disease specific survival. TRIAL REGISTRATION ClinicalTrials.gov NCT03543215, https://clinicaltrials.gov/ , date of registration: 30th June 2017.
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Affiliation(s)
- Xingfeng Li
- Department of Surgery and Cancer, Imperial College Hammersmith Campus, Du Cane Road, London, W12 0NN, UK
| | - Diana Marcus
- Department of Surgery and Cancer, Imperial College Hammersmith Campus, Du Cane Road, London, W12 0NN, UK
- Chelsea and Westminster Hospital, 369 Fulham Rd, London, SW10 9NH, UK
| | - James Russell
- The Imaging Department, Imperial College Healthcare NHS Trust, Hammersmith Hospital, Du Cane Road, London, W12 0HS, UK
| | - Eric O Aboagye
- Department of Surgery and Cancer, Imperial College Hammersmith Campus, Du Cane Road, London, W12 0NN, UK
| | - Laura Burney Ellis
- Department of Surgery and Cancer, Imperial College Hammersmith Campus, Du Cane Road, London, W12 0NN, UK
| | - Alexander Sheeka
- The Imaging Department, Imperial College Healthcare NHS Trust, Hammersmith Hospital, Du Cane Road, London, W12 0HS, UK
| | - Won-Ho Edward Park
- Imperial College Healthcare NHS Trust, Hammersmith Hospital, Du Cane Road, London, W12 0HS, UK
| | - Nishat Bharwani
- Department of Surgery and Cancer, Imperial College Hammersmith Campus, Du Cane Road, London, W12 0NN, UK
- The Imaging Department, Imperial College Healthcare NHS Trust, Hammersmith Hospital, Du Cane Road, London, W12 0HS, UK
| | - Sadaf Ghaem-Maghami
- Department of Surgery and Cancer, Imperial College Hammersmith Campus, Du Cane Road, London, W12 0NN, UK
| | - Andrea G Rockall
- Department of Surgery and Cancer, Imperial College Hammersmith Campus, Du Cane Road, London, W12 0NN, UK.
- The Imaging Department, Imperial College Healthcare NHS Trust, Hammersmith Hospital, Du Cane Road, London, W12 0HS, UK.
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A Flexible Bayesian Parametric Proportional Hazard Model: Simulation and Applications to Right-Censored Healthcare Data. JOURNAL OF HEALTHCARE ENGINEERING 2022; 2022:2051642. [PMID: 35693888 PMCID: PMC9184216 DOI: 10.1155/2022/2051642] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Revised: 03/24/2022] [Accepted: 04/18/2022] [Indexed: 11/17/2022]
Abstract
Survival analysis is a collection of statistical techniques which examine the time it takes for an event to occur, and it is one of the most important fields in biomedical sciences and other variety of scientific disciplines. Furthermore, the computational rapid advancements in recent decades have advocated the application of Bayesian techniques in this field, giving a powerful and flexible alternative to the classical inference. The aim of this study is to consider the Bayesian inference for the generalized log-logistic proportional hazard model with applications to right-censored healthcare data sets. We assume an independent gamma prior for the baseline hazard parameters and a normal prior is placed on the regression coefficients. We then obtain the exact form of the joint posterior distribution of the regression coefficients and distributional parameters. The Bayesian estimates of the parameters of the proposed model are obtained using the Markov chain Monte Carlo (McMC) simulation technique. All computations are performed in Bayesian analysis using Gibbs sampling (BUGS) syntax that can be run with Just Another Gibbs Sampling (JAGS) from the R software. A detailed simulation study was used to assess the performance of the proposed parametric proportional hazard model. Two real-survival data problems in the healthcare are analyzed for illustration of the proposed model and for model comparison. Furthermore, the convergence diagnostic tests are presented and analyzed. Finally, our research found that the proposed parametric proportional hazard model performs well and could be beneficial in analyzing various types of survival data.
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