2023
DOI: 10.1186/s12905-023-02202-9
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Assessing survival time of outpatients with cervical cancer: at the university of Gondar referral hospital using the Bayesian approach

Abstract: Background Cervical cancer is the 4th most common cancer in women worldwide. as well as the 4th most common cause of cancer-related death. The main objective of this study was to identify factors that affect the survival time of outpatients with cervical cancer. Methods A retrolective study including outpatients with cervical cancer was carried out in a hospital. To achieve the aim, 322 outpatients with cervical cancer were included in the study ba… Show more

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Cited by 10 publications
(9 citation statements)
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“…Furthermore, logistic regression does not account for censoring observation, i.e., it does not hold for time-to-event data, therefore, these statistical methods are unable to account for the hospital patient survival rate. In addition to the Cox regression model, other parametric models have also been employed to examine the survival distribution of outpatients with breast cancer, including exponential, log-logistic, and Weibull log-normal models [ 17 ]. The parametric survival models might be more suitable to describe the survival data if it is possible to identify the distribution of the survival time [ 18 ].…”
Section: Introductionmentioning
confidence: 99%
“…Furthermore, logistic regression does not account for censoring observation, i.e., it does not hold for time-to-event data, therefore, these statistical methods are unable to account for the hospital patient survival rate. In addition to the Cox regression model, other parametric models have also been employed to examine the survival distribution of outpatients with breast cancer, including exponential, log-logistic, and Weibull log-normal models [ 17 ]. The parametric survival models might be more suitable to describe the survival data if it is possible to identify the distribution of the survival time [ 18 ].…”
Section: Introductionmentioning
confidence: 99%
“…The Cox proportional hazard (PH) model is frequently used. However, a more flexible option is to apply generalized parametric models [20][21][22][23][24]. A particular model called ZBLN is highly regarded as the preferred option due to its flexibility to real life data.…”
Section: Discussionmentioning
confidence: 99%
“…This enables researchers to consider a broader and more diverse range of variables for examination, categorized by the cause of death. Therefore, Bayesian parametric models provide valuable tools for understanding the relationship between heart disease and survival outcomes [ 25 , 26 ].…”
Section: Discussionmentioning
confidence: 99%