جستجوی مقالات مرتبط با کلیدواژه "bayesian inference" در نشریات گروه "پزشکی"

Along with the increasing prevalence of ESRD in developing countries, the use of more up-to-date statistical models is highly recommended. It is crucial to control potential cure pattern and heterogenicity among patients.

In this longitudinal study, the data of 170 hemodialysis patients who visited the dialysis department of Shafa Hospital in Kerman from 2006 to 2016 were collected. To provides robust estimates the time to event data (death) were analyzed with a gamma frailty mixed cure Weibull model (MC-WG) using Bayesian inference.

About 49% of patients experienced the death and median survival time was 37.5 months. Older patients (0.264), female patients (0.269), and patients with higher mean serum urea levels (0.186) had a higher risk of death. Moreover, we observe a decrease in death with increase in Creatine (Cr).

In the MC-WG Bayesian model, the diabetes, AST, calcium, phosphorus and uric acid variables had a significant effect on the survival of hemodialysis patients, while they were not significant in the Cox PH model. The results of MC-WG Bayesian model are more consistent with other studies.

Bayesian mixture cure rate frailty model is a model used in survival analysis by controlling frailty when the fraction of cured individuals exists. The present study was performed as the first systematic review in survival analysis with cure fraction. The aim of this systematic review was to study and evaluate the related studies on Bayesian mixture cure rate frailty model. Also, this model was used to demonstrate its importance and applicability in determining the variables affecting the survival of patients with gastric cancer.

This systematic review was done based on the PRISMA guideline by considering related searching keywords in PubMed, Scopus, Science Direct, Web of Science, and Google Scholar. Also, Bayesian mixture cure rate frailty model was used to analyze gastric cancer data.

In the beginning, 882 studies related to survival analysis of cure rate model were found. Finally, by reading the full-text, only 4 related studies were found based on the inclusion and exclusion criteria. In these studies, semi-parametric models and parametric model with Weibull distribution were used for time-to-event data. Also, based on the results of the model, significant and affective variables on the survival of patients with gastric cancer were found.

According to the results of this study, in the cure model, choice of proper distribution for the frailty variable and baseline distribution can influence the results. It was also found that place of residence, chemotherapy, morphology, and metastasis are effective variables on survival of patients with gastric cancer.

The aim of this study was to apply the Bayesian mixture cure rate frailty model to determine the factors that influence shortterm and long-term survival of patients with gastric cancer

Determining the risk factors of gastric cancer is currently considered very important, because the disease has become one of the most dangerous types of mortal cancers. Therefore, it is possible to determine the effective risk factors of short-term and long-term survival in patients through utilizing this model.

The present retrospective study was conducted on 339 gastric cancer patients whose data was recorded in hospitals of Kerman province, Iran, during 2001-2015. In the study, the Bayesian mixture cure rate frailty model was used to determine the effective factors of short-term and long-term survival in patients.

In the present study, the event of interest occurred for 57.5% of patients. Over time, the survival rate of cancer patients reached its lowest point, approximately 0.3 at the end of study. According to the results of the present study, variables of chemotherapy (β=-0.35 (-0.75, - 0.03) and OR=1.59 (1.08, 2.19)), morphology (β =-0.98(-1.45, -0.48) and OR=2.99 (1.78, 4.17)), and metastasis (β =0.42(0.10, 0.93) and OR=0.39(0.01, 0.84)) were identified as effective factors in short-term and long-term survival of patients.

The effective factors of long-term and short-term survival can be identified by utilizing the Bayesian mixture cure rate frailty model, while it is impossible through conventional models of survival analysis. Chemotherapy, morphology, and metastasis are the most important effective factors of short-term and long-term survival in patients with gastric cancer.

The Bayesian methods have received more attention in medical research. It is considered as a natural paradigm for dealing with applied problems in the sciences and also an alternative to the traditional frequentist approach. However, its concept is somewhat difficult to grasp by nonexperts. This study aimed to explain the foundational ideas of the Bayesian methods through an intuitive example in medical science and to illustrate some simple examples of Bayesian data analysis and the interpretation of results delivered by Bayesian analyses. In this study, data sparsity, as a problem which could be solved by this approach, was presented through an applied example. Moreover, a common sense description of Bayesian inference was offered and some illuminating examples were provided for medical investigators and nonexperts.

Data augmentation prior, MCMC, and Bayes factor were introduced. Data from the Khuzestan study, a 2-phase cohort study, were applied for illustration. Also, the effect of vitamin D intervention on pregnancy outcomes was studied.

Unbiased estimate was obtained by the introduced methods.

Bayesian and data augmentation as the advanced methods provide sufficient results and deal with most data problems such as sparsity.