Parametric survival models with interval censored data in determining prognostic factors of patients of lung cancer

In clinical trials, biological research and medical studies that involved periodically follow-ups, it is predominently to have censored data. The censored data can be either left, right or interval censored where it reflects on the uncertainty of survival time until an event occur. Survival analysis...

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Main Author:	Muhamad Jamil, Siti Afiqah
Format:	Thesis
Language:	English English English
Published:	2020
Subjects:	TK7800-8360 Electronics
Online Access:	http://eprints.uthm.edu.my/1172/1/24p%20SITI%20AFIQAH%20BINTI%20MUHAMAD%20JAMIL.pdf http://eprints.uthm.edu.my/1172/2/SITI%20AFIQAH%20BINTI%20MUHAMAD%20JAMIL%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/1172/3/SITI%20AFIQAH%20BINTI%20MUHAMAD%20JAMIL%20WATERMARK.pdf
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spelling	my-uthm-ep.11722021-09-30T06:17:37Z Parametric survival models with interval censored data in determining prognostic factors of patients of lung cancer 2020-10 Muhamad Jamil, Siti Afiqah TK7800-8360 Electronics In clinical trials, biological research and medical studies that involved periodically follow-ups, it is predominently to have censored data. The censored data can be either left, right or interval censored where it reflects on the uncertainty of survival time until an event occur. Survival analysis can accommodates both fixed and time varying covariates with the presence of censored data. The survival time of parametric distribution of Weibull, exponential and log-logistic were derived by using the inverse cumulative distribution function with the hazard and survival function. Standard estimation values such as, the mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), and mean asolute percentage error (MAPE) were employed in comparing different distributions and number of sample sizes. Besides, Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), Corrected Akaike Information Criterion (AICC) been evaluated in finding the best fit model towards the survival time of lung cancer. Thus, the exponential model was found to be the most reliable with censored and fixed covariate of simulation and lung cancer data while the log-logistic appeared to be practically more stable than Weibull in estimating the censored with varying effect covariate. Meanwhile, the prognostic factors that were significant involved the types of lung cancer, gender and some other interactions. Somehow, increased number of sample either in simulation or bootstrap makes the results to be approximately more reliable as the biases decreased. 2020-10 Thesis http://eprints.uthm.edu.my/1172/ http://eprints.uthm.edu.my/1172/1/24p%20SITI%20AFIQAH%20BINTI%20MUHAMAD%20JAMIL.pdf text en public http://eprints.uthm.edu.my/1172/2/SITI%20AFIQAH%20BINTI%20MUHAMAD%20JAMIL%20COPYRIGHT%20DECLARATION.pdf text en staffonly http://eprints.uthm.edu.my/1172/3/SITI%20AFIQAH%20BINTI%20MUHAMAD%20JAMIL%20WATERMARK.pdf text en validuser phd doctoral Universiti Tun Hussein Onn Malaysia Faculty of Applied Science and Technology
institution	Universiti Tun Hussein Onn Malaysia
collection	UTHM Institutional Repository
language	English English English
topic	TK7800-8360 Electronics
spellingShingle	TK7800-8360 Electronics Muhamad Jamil, Siti Afiqah Parametric survival models with interval censored data in determining prognostic factors of patients of lung cancer
description	In clinical trials, biological research and medical studies that involved periodically follow-ups, it is predominently to have censored data. The censored data can be either left, right or interval censored where it reflects on the uncertainty of survival time until an event occur. Survival analysis can accommodates both fixed and time varying covariates with the presence of censored data. The survival time of parametric distribution of Weibull, exponential and log-logistic were derived by using the inverse cumulative distribution function with the hazard and survival function. Standard estimation values such as, the mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), and mean asolute percentage error (MAPE) were employed in comparing different distributions and number of sample sizes. Besides, Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), Corrected Akaike Information Criterion (AICC) been evaluated in finding the best fit model towards the survival time of lung cancer. Thus, the exponential model was found to be the most reliable with censored and fixed covariate of simulation and lung cancer data while the log-logistic appeared to be practically more stable than Weibull in estimating the censored with varying effect covariate. Meanwhile, the prognostic factors that were significant involved the types of lung cancer, gender and some other interactions. Somehow, increased number of sample either in simulation or bootstrap makes the results to be approximately more reliable as the biases decreased.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Muhamad Jamil, Siti Afiqah
author_facet	Muhamad Jamil, Siti Afiqah
author_sort	Muhamad Jamil, Siti Afiqah
title	Parametric survival models with interval censored data in determining prognostic factors of patients of lung cancer
title_short	Parametric survival models with interval censored data in determining prognostic factors of patients of lung cancer
title_full	Parametric survival models with interval censored data in determining prognostic factors of patients of lung cancer
title_fullStr	Parametric survival models with interval censored data in determining prognostic factors of patients of lung cancer
title_full_unstemmed	Parametric survival models with interval censored data in determining prognostic factors of patients of lung cancer
title_sort	parametric survival models with interval censored data in determining prognostic factors of patients of lung cancer
granting_institution	Universiti Tun Hussein Onn Malaysia
granting_department	Faculty of Applied Science and Technology
publishDate	2020
url	http://eprints.uthm.edu.my/1172/1/24p%20SITI%20AFIQAH%20BINTI%20MUHAMAD%20JAMIL.pdf http://eprints.uthm.edu.my/1172/2/SITI%20AFIQAH%20BINTI%20MUHAMAD%20JAMIL%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/1172/3/SITI%20AFIQAH%20BINTI%20MUHAMAD%20JAMIL%20WATERMARK.pdf
_version_	1747830749845782528

Parametric survival models with interval censored data in determining prognostic factors of patients of lung cancer

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