Comparison of parametric and semi-parametric models with randomly right-censored data by weighted estimators: Two applications in colon cancer and hepatocellular carcinoma datasets

Abstract

In this study, parametric and semi-parametric regression models are examined for random right censorship. The components of the aforementioned regression models are estimated with weights based on Cox and Kaplan–Meier estimates, which are semi-parametric and nonparametric methods used in survival analysis, respectively. The Tobit based on weights obtained from a Cox regression is handled as a parametric model instead of other parametric models requiring distribution assumptions such as exponential, Weibull, and gamma distributions. Also, the semi-parametric smoothing spline and the semi-parametric smoothing kernel estimators based on Kaplan–Meier weights are used. Therefore, estimates are obtained from two models with flexible approaches. To show the flexible shape of the models depending on the weights, Monte Carlo simulations are conducted, and all results are presented and discussed. Two empirical datasets are used to show the performance of the aforementioned estimators. Although three approaches gave similar results to each other, the semi-parametric approach was slightly superior to the parametric approach. The parametric approach method, on the other hand, yields good results in medium and large sample sizes and at a high censorship level. All other findings have been shared and interpreted.

Weibull parametric model for survival analysis in women with endometrial cancer using clinical and T2-weighted MRI radiomic features

“…Although Weibull model has been applied to survival analysis for the patient with lung cancer study [ 31 ] and gastric cancer [ 32 ], to the best of our knowledge, this is the first time to apply Weibull parametric model with MRI radiomic features for survival analysis for the patients with EC. Using colon cancer patient data, a previous study [ 33 ] compared semi-parametric methods with parametric models based on Monte Carlo simulations study, even though semi-parametric models performed slightly better than the parametric approach, parametric models were superior to the semi-parametric model based on large dataset. However, another study shows the parametric model is generally better than the semi-parametric model for the survival study in cancer study [ 11 ].…”

“…Finally, although we included 491 cases in this study, the total number of patients with death is still small ( n = 95 in training dataset). Larger dataset will be helpful to improve Weibull model fitting, as previous study showed that parametric model is superior to semi-parametric model when dataset is large for the estimation [ 33 ].…”

Modified Local Linear Estimators in Partially Linear Additive Models with Right-Censored Data Based on Different Censorship Solution Techniques

“…Ref. [ 6 ] also studied this dataset, comparing parametric and semiparametric models on right-censored data. However, their study focused on a semiparametric model with a univariate nonparametric component using the covariate age.…”

“…There are several important studies on the estimation of model (1) under right-censored data, as given in (2), such as refs. [ 4 , 5 , 6 ], among others.…”

“…There are several important studies on the estimation of model ( 1) under right-censored data, as given in (2), such as refs. [4][5][6], among others. While model (1) offers reliable performance for both censored and uncensored data due to its ability to incorporate both parametric and nonparametric components, it encompasses only a singular nonparametric component.…”