copula function

Nonparametric regression can usually be used when the distribution of the dependent variable does not follow the property of normality. In this study, due to the nature of the variables, a bivariate Capula regression model was used to identify the factors affecting the liver enzymes (ALT and AST) and the relationship between these enzymes. This type of regression is suitable when the response variables have a relatively high degree of skewness and interdependence.

In this cross-sectional study, a sample of 2000 participants in the Shahrekord cohort study were randomly selected. To achieve the Capula regression model, the inverse Gaussian margin distribution and the Gumble joint function were selected according to the Akaike criterion. Gamlss, Copula, and Ggrm statistical packages were used in the R software.
(Ethic code: 3316)

According to the findings, some variables were identified as effective factors on the concentration of ALT and AST enzymes through marginal distribution parameters and Capula function. Blood urea, triglyceride, GGT, ALP, and BMI had a nonlinear and significant effect on the mean concentration of the ALT enzyme. The BMI, GGT, ALP, LDL, and HDL (nonlinearly), as well as blood urea (linearly), had a significant effect on the mean concentration of AST enzyme. Finally, the variables of BMI, triglycerides, GGT, and ALP affect the relationship between the concentration levels of the liver enzymes (ALT and AST).

Using this model, in addition to identifying the effective factors, it is possible to distinguish between linear and nonlinear relationships between independent and dependent variables.

Wide verity of causes of death exist in ageing societies. It is suitable to apply competing risk models in order to investigate and analyze time to death in the target population. The aim of this study was to identify some factors effective on survival of the elderly living in nursing home using Copula Competing Risk Model with Bayesian approach. In a descriptive-longitudinal study، data were collected from 510 elderly’s medical documents in Kashan Golabchi elderly nursing home. To identify effective factors associated with time to death of the elderly، medical diagnosis of cause of death cited in elderly’s documents were considered. Later، Competing Risk Model using Clayton Copula Function based on Bayesian approach was fitted and Credible intervals were estimated. To analyze the data WinBugs Software was used. In univariate analysis، being in the age at the beginning of reception، hypertension، family history of cardiovascular diseases، history of myocardial infarction، history of stroke were significant for time to death due to cardiovascular diseases in the elderly. In multivariate analysis، age group 75 to 90 years at the beginning of the reception (HR=2. 1; CI 95%= (1. 26، 3. 67)) and history of stroke (HR=2; CI 95%= (1. 28، 3. 32)) were significant for time to death due to cardiovascular diseases in the elderly. One of the benefits of statistical modeling is the ability to generalize its results. According to the results، some crucial factors accelerating the time of death in elderly were identified. Therefore، it is highly recommended that، in therapeutic and preventive actions in order to increase the survival time for the elderly the significant studied factors should be considered.

In the common Cox proportional hazards model one of the basic assumptions is independence between censoring time and event time. In clinical studies, when censoring is caused by competing risks or patient withdrawal, there is always a concern about the validity of treatment effect estimates that are obtained under the assumption of independent censoring. Introduction a solution for checking the assumption of independent and extending the Cox model for dependent censoring is useful.
Material & To achieve this goal, we use copula function and extend the Cox model. This model can also perform a sensitivity analysis for checking the assumption of independent. We generalize the likelihood function in R software and estimate the parameters by using a iteration algorithm. We apply the proposed method to the data of breast cancer patients at Ghaem and Omid hospitals and check the independence assumption. We show by Simulation that this algorithm works well and show the effect of dependent censoring on estimated parameters. We apply the proposed method to the data of breast cancer patients at Ghaem and Omid hospitals and show that the independence assumption is true. Not assumption of independence causes bias in estimation in Cox model and the bias is dependent on the degree of association between event and censoring times. In dataset of breast cancer patients the primary diagnosis is an important role in Success in the treatment.

In the common Cox proportional hazards model one of the basic assumptions is independence between censoring time and event time. In clinical studies, when censoring is caused by competing risks or patient withdrawal, there is always a concern about the validity of treatment effect estimates that are obtained under the assumption of independent censoring. Introduction a solution for checking the assumption of independent and extending the Cox model for dependent censoring is useful. To achieve this goal, we use copula function and extend the Cox model. This model can also perform a sensitivity analysis for checking the assumption of independent. We generalize the likelihood function in R software and estimate the parameters by using a iteration algorithm. We apply the proposed method to the data of breast cancer patients at Ghaem and Omid hospitals and check the independence assumption. We show by Simulation that this algorithm works well and show the effect of dependent censoring on estimated parameters. We apply the proposed method to the data of breast cancer patients at Ghaem and Omid hospitals and show that the independence assumption is true. Not assumption of independence causes bias in estimation in Cox model and the bias is dependent on the degree of association between event and censoring times. In dataset of breast cancer patients the primary diagnosis is an important role in Success in the treatment.