A Comparative Study of Life Time Models in the Analysis of Survival Data

Vallinayagam, V.; Parthasarathy, Srikanth; Venkatesan, P

doi:10.15373/2249555x/jan2014/101

Cited by 3 publications

(3 citation statements)

References 2 publications

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“…Sharma et al (2019), pointed out in their study to compare the efficiency of some accelerated failure time models (lognormal, exponential, log-logistic, and weibull) AIC was calculated and Weibull model found to be the best for the breast cancer data set, which is consistent with our findings where the weibull was the best fitted model among the models considered Vallinayagam et al (2014). compared the performance of some parametric models including log-logistic, gompertz, exponential lognormal and weibull for Breast cancer data set.…”

supporting

confidence: 90%

Study on the Performance of Some Parametric Proportional Hazard Models and Semi-Parametric Model in the Analysis of Breast Cancer Data

Zoramawa,

Usman,

Magaji

et al. 2023

FJS

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A study is conducted on medical records of 416 breast cancer patients. Analysis was performed using the R software version R3.6.3, and the level of significance was set at 0.05. The work employed three models which were based on Exponential, Weibull and Cox Regression models. The Weibull proportional model (AIC=1959.038) was the most appropriate model among the considered models, based on the Akaike information criterion (AIC). Results of the best fitted model showed that the survival time of breast cancer patients is significantly affected by age, age at diagnosis, and treatment taken at 95%.

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supporting

confidence: 90%

Study on the Performance of Some Parametric Proportional Hazard Models and Semi-Parametric Model in the Analysis of Breast Cancer Data

Zoramawa,

Usman,

Magaji

et al. 2023

FJS

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“…The log‐Normal distribution is adopted, which is popular in published studies (Klein & Moeschberger, 2003; Shokoohi et al., 2019). It has been observed that for many diseases including breast cancer (Vallinayagam et al., 2014) and NSCLC (Claret et al., 2018), the log‐Normal distribution fits better than other parametric models. In addition, we can specify conjugate priors for log‐Normal distribution, drastically simplifying estimation.…”

Section: Methodsmentioning

confidence: 99%

Two-Level Bayesian Interaction Analysis for Survival Data Incorporating Pathway Information

Qin

2022

Biometrics

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Genetic interactions play an important role in the progression of complex diseases, providing explanation of variations in disease phenotype missed by main genetic effects. Comparatively, there are fewer studies on survival time, given its challenging characteristics such as censoring. In recent biomedical research, two‐level analysis of both genes and their involved pathways has received much attention and been demonstrated as more effective than single‐level analysis. However, such analysis is usually limited to main effects. Pathways are not isolated, and their interactions have also been suggested to have important contributions to the prognosis of complex diseases. In this paper, we develop a novel two‐level Bayesian interaction analysis approach for survival data. This approach is the first to conduct the analysis of lower‐level gene–gene interactions and higher‐level pathway–pathway interactions simultaneously. Significantly advancing from the existing Bayesian studies based on the Markov Chain Monte Carlo (MCMC) technique, we propose a variational inference framework based on the accelerated failure time model with effective priors to accommodate two‐level selection as well as censoring. Its computational efficiency is much desirable for high‐dimensional interaction analysis. We examine performance of the proposed approach using extensive simulation. The application to TCGA melanoma and lung adenocarcinoma data leads to biologically sensible findings with satisfactory prediction accuracy and selection stability.

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“…Amongst the parametric survival models, Lognormal has been selected as the best fitting model for cancer data (Vallinayagam et al, 2014;Mohseny et al, 2017). Awodutire et al (2017) used parametric survival models to investigate the survival times of the Nigerian cancer data.…”

Section: Related Workmentioning

confidence: 99%

Survival Times of Breast Cancer Patients in Nigeria: Application of Cox and Parametric Survival Models

F.C.,

C.C.,

U.M.

et al. 2023

African Journal of Biology and Medical Research

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In this study, we modeled the survival time of breast cancer patients in Nigeria using five survival models, namely the Cox model, the exponential model, the lognormal model, the logistic model, and the Weibull model. The Akaike information criterion (AIC) and Bayesian information criterion (BIC) were used as performance metrics for the selection of the best-fit model. The Cox proportional hazard (CPH) model was the best model for the cancer data. We also noted that the median patient survival time was 295 days. The Kaplan-Meier test was used to compare the survival curves. The CPH model was used to model the data. We observed that the neoadjuvant therapy covariate had a significant effect on the survival time of the breast cancer patients (p < 0.05). This suggests that it has a considerable impact on Nigerian breast cancer patients' survival rates. This study could result in more efficient cancer treatments and has substantial implications for the management and care of breast cancer patients in Nigeria. It further extends the work of Awodutire et al. (2017).

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A Comparative Study of Life Time Models in the Analysis of Survival Data

Cited by 3 publications

References 2 publications

Study on the Performance of Some Parametric Proportional Hazard Models and Semi-Parametric Model in the Analysis of Breast Cancer Data

Study on the Performance of Some Parametric Proportional Hazard Models and Semi-Parametric Model in the Analysis of Breast Cancer Data

Two-Level Bayesian Interaction Analysis for Survival Data Incorporating Pathway Information

Survival Times of Breast Cancer Patients in Nigeria: Application of Cox and Parametric Survival Models

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