Comparison performance of the Bayesian Approach with the Weibull and Birnbaum-Saunders distributions in imputation of time-to-event censors.

Almost all survival data is censored, and censor imputation is necessary. This study aimed to investigate the performance of the Bayesian Approach (BA) in the imputation of censored records in simulated and Breast Cancer (BC) data. Due to the difference in the distribution of time to event in survival analysis, two well-known the Weibull and Birnbaum-Saunders (BS) distributions have been used to test the performance of the BA. For each of the censored, 10,000 times were simulated using the BA in R and BUGS software, and their median or mean was imputed instead of each censor. The eligibility of both imputation methods was investigated using different curves, different censoring percentages, and sample sizes, as well as the Deviance Information Criteria (DIC), Effective Sample Size, and the Geweke diagnostic in simulated and especially real BC data. The BC data, which contains 220 patients who were identified and followed up between 2015 and 2023, was made accessible on February 1, 2023. The Kaplan-Meier, the BA, and other survival curves were drawn for the observed times. Findings indicated that the performance of the BA under the Weibull and BS distributions in simulated data is similar. The DIC index in the BC data under the BS distribution (1510) is less than the Weibull distribution (1698). Therefore, the BS distribution is preferred over the Weibull for imputation of censoring times in real BC data.

Survival analysis of recurrent breast cancer patients using mix Bayesian network.

Introduction: Breast cancer (BC) is the most common cancer among women. Iranians have an 11% BC recurrence rate, which lowers their survival rates. Few studies have investigated cancer recurrence survival rates. This study's major purpose is to use a mixed Bayesian network (BN) to analyze recurrent patients' survival. Material and methods: This study aimed to evaluate the pathobiological features, age, gender, final status, and survival time of the patients. Bayesian imputation was used for missing data. The performance of BN was optimized through the utilization of a blacklist and prior probability. After structural and parametric learning, posterior conditional probabilities and mean survival periods for the node arcs were predicted. The hold-out technique based on the posterior classification error was used to investigate the model's validation. Results: The study included 220 cancer recurrence patients. These patients averaged 47 years old. The BN with a blacklist and prior probability has a higher network score than other networks. The hold-out technique verified structural learning. The Directed Acyclic Graph showed a statistically significant relationship between cancer biomarkers (ER, PR, and HER2 receptors), cancer stage, and tumor grade and patient survival duration. Patient death was also significantly associated with education, ER, PR, HER2, and tumor grade. The BN reports that HER2 negative, ER positive, and PR positive patients had a higher survival rate. Conclusion: Survival and death of relapsed patients depend on biomarkers. Based on the findings, patient survival can be predicted with their features.

The Evaluation of Results of Twenty Common Equations for Differentiation of Beta Thalassemia Trait from Iron Deficiency Anemia: A Cross-Sectional Study.

Background: Beta Thalassemia Trait (BTT) and Iron Deficiency Anemia (IDA) were two common clinical problems with clinical hypochromic and microcytic manifestations, and their differentiation from each other was very important and needs innovative formulas and laboratory tests. Since the consideration of anemia as a pair with BTT leads to beta-thalassemia major birth in 25% of cases, offering prospective parents detailed information about the likelihood of their offspring developing BTT is essential. The present study aimed to investigate the performance of common equations in differentiation of BTT from IDA. Methods: In the present cross-sectional study, twenty common equations were selected in the differentiation of BTT from IDA. To evaluate the equations, the tests of 292 individuals (73 individuals with BTT and 219 individuals with IDA) were compared with the initial diagnosis of hypochromic and microcytic anemia using the formulas. Descriptive and value indices and Roc curve were utilized for all equations to analyze the results. Results: Among twenty differential equations, Bordbar, Kerman I, II and Srivastava equations had the highest area under Roc curve (AUC) of 0.841, 0.838, 0.836, and 0.830 respectively, but Kandhro I. equation had the lowest AUC (0.378). Conclusion: Given the importance of AUC and value indices of differential equations in clinical decision making, and results of evaluating common equations in differentiation of BTT from IDA. It is essential to improve the values of the equations by re-examining the parameters involved in them.

Effects of probiotic and alpha-lipoic acid supplements, separately or in combination on the anthropometric indicators and maintenance of weight in overweight individuals.

BACKGROUND & AIMS: Weight loss after proper diet is one of the main topics in nutrition. This study was designed to evaluate the effects of probiotic and alpha-Lipoic acid (ALA) supplements on the anthropometric indicators and maintenance of weight in overweight individuals. METHODS: This study consisted of two phases of weight loss (8 weeks) and weight maintenance (16 weeks). Eighty-eight overweight participants were randomly divided into 4 groups in phase 1: isocaloric diet with probiotic (500 mg), an isocaloric diet with ALA (600 mg) and probiotic, an isocaloric diet with ALA and isocaloric diet with placebo. In phase 2, participants received a normal diet with the mentioned supplements. In the beginning, end of the phase 1, and at the end of phase 2, weight, body mass index (BMI), waist circumference (WC), hip circumference (HC), body fat percentage, and blood pressure (BP) were measured. Also, 10 cc blood samples were taken from subjects to measure C-reactive protein (CRP). Data was analyzed using SPSS software. RESULTS: At the end of the two phases, the differences of changes in the probiotic + ALA group was significant in weight, WC, and CRP factors when compared to the other groups (P < 0.05). Also, at the end of the study, maintain a reduced weight was significantly higher in the probiotic + ALA group than in the other groups (P < 0.05). CONCLUSION: According to findings, probiotics and ALA supplementation with normal diet help to maintain decreased weight after adhering to a weight loss diet. This may be due to the reduction of inflammation. TRIAL REGISTRATION: (IRCT20141025019669N10).

Comparison Cure Rate Models by DIC Criteria in Breast Cancer Data

Background: One of the malignant tumors is Breast Cancer (BC) that starts in the cells of breast. There is many models for survival analysis of patients such as Cox PH model, Parametric models etc. But some disease are that all of patients will not experience main event then usual survival model is inappropriate. In addition, In the presence of cured patients, if researcher can specify distribution of survival time, usually cure rate models are preferable to parametric models. Distribution of Survival time can be Weibull, Log normal, Logistic, Gamma and so. Comparison of Weibull, Log normal and Logistic distribution for finding the best distribution of survival time is purpose of this study. Material and Methods: Among 787 patients with BC by Cancer Research Center recognized and followed from 1985 until 2013. Variables stage of cancer, age at diagnosis, tumor size and Number of Removed Positive Lymph Nodes (NRPLN) for fitting Cure rate model were selected. The best model selected with DIC criteria. All analysis were performed using SAS 9.2. Results: Mean (SD) of age was 48.47 (11.49) years and Mean of survival time and Maximum follow up time was 326 and 55.12 months respectively. During following patients, 145 (18.4%) patients died from BC and others survived (censored). Also, 1-year, 5-year and 10-year survival rate was 94, 77 and 56 percent respectively. Log normal model with smaller DIC were selected and fitted. All of mentioned variables in the model were significant on cure rate. Conclusion: This study indicated that survival time of BC followed from Log normal distribution in the best way.

Fitting Cure Rate Model to Breast Cancer Data of Cancer Research Center.

BACKGROUND: The Cox PH model is one of the most significant statistical models in studying survival of patients. But, in the case of patients with long-term survival, it may not be the most appropriate. In such cases, a cure rate model seems more suitable. The purpose of this study was to determine clinical factors associated with cure rate of patients with breast cancer. MATERIALS AND METHODS: In order to find factors affecting cure rate (response), a non-mixed cure rate model with negative binomial distribution for latent variable was used. Variables selected were recurrence cancer, status for HER2, estrogen receptor (ER) and progesterone receptor (PR), size of tumor, grade of cancer, stage of cancer, type of surgery, age at the diagnosis time and number of removed positive lymph nodes. All analyses were performed using PROC MCMC processes in the SAS 9.2 program. RESULTS: The mean (SD) age of patients was equal to 48.9 (11.1) months. For these patients, 1, 5 and 10-year survival rates were 95, 79 and 50 percent respectively. All of the mentioned variables were effective in cure fraction. Kaplan-Meier curve showed cure model's use competence. CONCLUSIONS: Unlike other variables, existence of ER and PR positivity will increase probability of cure in patients. In the present study, Weibull distribution was used for the purpose of analysing survival times. Model fitness with other distributions such as log-N and log-logistic and other distributions for latent variable is recommended.

Prognostic factors for survival in patients with breast cancer referred to Cancer Research Center in Iran.

BACKGROUND: Breast cancer is a malignant tumor that starts from cells of the breast and is seen mainly in women. It's the most common cancer in women worldwide and is a major threat to health. The purpose of this study was to fit a Cox proportional hazards model for prediction and determination of years of survival in Iranian patients. MATERIALS AND METHODS: A total of 366 patients with breast cancer in the Cancer Research Center were included in the study. A Cox proportional hazard model was used with variables such as tumor grade, number of removed positive lymph nodes, human epidermal growth factor receptor 2 (HER2) expression and several other variables. Kaplan-Meier curves were plotted and multi-years of survival were evaluated. RESULTS: The mean age of patients was 48.1 years. Consumption of fatty foods (p=0.033), recurrence (p<0.001), tumor grade (p=0.046) and age (p=0.017) were significant variables. The overall 1- year, 3-year and 5-year survival rates were found to be 93%, 75% and 52%. CONCLUSIONS: Use of covariates and the Cox proportional hazard model are effective in predicting the survival of individuals and this model distinguished 4 effective factors in the survival of patients.