Measuring the performances of covariates using exponential survival analysis with partly-interval censored simulation data
In many fields of science, modelling and analyzing survival rates has shown to be a valuable element of statistical study. This paper aims at proposing the partly-interval censored data into the fixed and time-varying covariates and measure the performances of Exponential survival distribution usin...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2024
|
| Subjects: | |
| Online Access: | http://eprints.uthm.edu.my/11965/1/Measuring%20the%20performances%20of%20covariates.pdf http://eprints.uthm.edu.my/11965/ https://doi.org/10.1063/5.0194223 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | In many fields of science, modelling and analyzing survival rates has shown to be a valuable element of statistical
study. This paper aims at proposing the partly-interval censored data into the fixed and time-varying covariates and measure the performances of Exponential survival distribution using mean square error (MSE), mean bias error (MBE), mean
absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE) and standard error. As a
result, when dealing the data without censored observations, the exponential distribution significantly fit the simulation
data since low values of error measurements appeared when the data included the exact and complete types of simulation.
Thus, this study proposed that the uncensored data could be applicable towards the Exponential survival distribution
compared to other distributions of survival analysis. |
|---|