Mukesh Kumar1, Prashant Kr Sonker1, Agni Saroj1, Aanchal Jain2, Atanu Bhattacharjee3, Rakesh Kr Saroj4. 1. Department of Statistics, M.M.V, Banaras Hindu University, Varanasi, India. 2. CRS, Tata Memorial Hospital, Mumbai, India. 3. Section of Biostatistics, Centre for Cancer Epidemiology, Tata Memorial Centre, India. 4. Centre for Infectious Disease Research, Lusaka, Zambia.
Abstract
BACKGROUND: Cox regression is the most widely used survival model in oncology. Parametric survival models are an alternative of Cox regression model. In this study, we have illustrated the application of semiparametric model and various parametric (Weibull, exponential, log-normal, and log-logistic) models in lung cancer data by using R software. AIMS: The aim of the study is to illustrate responsible factors in lung cancer and compared with Cox regression and parametric models. METHODS: A total of 66 lung cancer patients of African Americans (AAs) (data available online at http://clincancerres.aacrjournals.org) was used. To identify predictors of overall survival, stage of patient, sex, age, smoking, and tumor grade were taken into account. Both parametric and semiparametric models were fitted. Performance of parametric models was compared by Akaike information criterion (AIC). "Survival" package in R software was used to perform the analysis. Posterior density was obtained for different parameters through Bayesian approach using WinBUGS. RESULTS: The illustration about model fitting problem was documented. Parametric models were fitted only for stage after controlling for age. AIC value was minimum (462.4087) for log-logistic model as compared with other parametric models. Log-logistic model was the best fit for AAs lung cancer data under study. CONCLUSION: Exploring parametric survival models in daily practice of cancer research is challenging. It may be due to many reasons including popularity of Cox regression and lack of knowledge about how to perform it. This paper provides the application of parametric survival models by using freely available R software with illustration. It is expected that this present work can be useful to apply parametric survival models.
BACKGROUND:Cox regression is the most widely used survival model in oncology. Parametric survival models are an alternative of Cox regression model. In this study, we have illustrated the application of semiparametric model and various parametric (Weibull, exponential, log-normal, and log-logistic) models in lung cancer data by using R software. AIMS: The aim of the study is to illustrate responsible factors in lung cancer and compared with Cox regression and parametric models. METHODS: A total of 66 lung cancerpatients of African Americans (AAs) (data available online at http://clincancerres.aacrjournals.org) was used. To identify predictors of overall survival, stage of patient, sex, age, smoking, and tumor grade were taken into account. Both parametric and semiparametric models were fitted. Performance of parametric models was compared by Akaike information criterion (AIC). "Survival" package in R software was used to perform the analysis. Posterior density was obtained for different parameters through Bayesian approach using WinBUGS. RESULTS: The illustration about model fitting problem was documented. Parametric models were fitted only for stage after controlling for age. AIC value was minimum (462.4087) for log-logistic model as compared with other parametric models. Log-logistic model was the best fit for AAs lung cancer data under study. CONCLUSION: Exploring parametric survival models in daily practice of cancer research is challenging. It may be due to many reasons including popularity of Cox regression and lack of knowledge about how to perform it. This paper provides the application of parametric survival models by using freely available R software with illustration. It is expected that this present work can be useful to apply parametric survival models.
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