Survival Analysis: The Cox Proportional Hazard Model
Survival analysis, is in a class of statistical methods, for considering the occurrence and timing of events is applied, in field of medical sciences1.
There is more than one modeling approach for analyzing data 2. But before the choosing the best method, it seems that, it is suitable, but the basic question will be that: why this method? Modeling procedure will answer to this question3.
One of the most widely used models, in survival analysis, is the Cox proportional hazards model4. The main assumption of the Cox regression model is proportional hazards. It means that, the hazard for an individual, is proportional to the hazard, for any other individual, or the hazard ratio, is constant over time5. If PH assumptions, be violated, extended …show more content…
Accordingly, at least of 50 percent of breast cancer recurrences in woman, will happen 5 years after first diagnosis4. In researches of breast cancer have been indicated that, CEA and CA15-3, are the two most broadly used tumor markers in the clinical fields for more than 30 years20. In recent years, many studies have shown that, circulating tumor markers, are important diagnostic instrument, in under surveillance of breast cancer patients8, also it can be used, for surveillance goals in clinical studies …show more content…
Moreover, histological grading, stage, age and hormone receptor status and Her2 receptor level were evaluated at the time of diagnosis8-11. Hormone therapy during treatment were also considered.
The data were analyzed by SAS software ver9 and level of significance equivalent to 0.05. Observations with missing data were removed from data analysis with SAS program.
1-Statistical analysis
The most common approach to determine the covariate effects on survival is the Cox regression model. Although the model is based on the assumption of proportional hazards, no particular form of probability distribution is considered for the survival times. The model is therefore mentioned to as a semi-parametric model.
Let x1, x2, . . . . . ,x p be the values of p covariates. According to the Cox regression model, the hazard function is given as follows 5,12
There is more than one modeling approach for analyzing data 2. But before the choosing the best method, it seems that, it is suitable, but the basic question will be that: why this method? Modeling procedure will answer to this question3.
One of the most widely used models, in survival analysis, is the Cox proportional hazards model4. The main assumption of the Cox regression model is proportional hazards. It means that, the hazard for an individual, is proportional to the hazard, for any other individual, or the hazard ratio, is constant over time5. If PH assumptions, be violated, extended …show more content…
Accordingly, at least of 50 percent of breast cancer recurrences in woman, will happen 5 years after first diagnosis4. In researches of breast cancer have been indicated that, CEA and CA15-3, are the two most broadly used tumor markers in the clinical fields for more than 30 years20. In recent years, many studies have shown that, circulating tumor markers, are important diagnostic instrument, in under surveillance of breast cancer patients8, also it can be used, for surveillance goals in clinical studies …show more content…
Moreover, histological grading, stage, age and hormone receptor status and Her2 receptor level were evaluated at the time of diagnosis8-11. Hormone therapy during treatment were also considered.
The data were analyzed by SAS software ver9 and level of significance equivalent to 0.05. Observations with missing data were removed from data analysis with SAS program.
1-Statistical analysis
The most common approach to determine the covariate effects on survival is the Cox regression model. Although the model is based on the assumption of proportional hazards, no particular form of probability distribution is considered for the survival times. The model is therefore mentioned to as a semi-parametric model.
Let x1, x2, . . . . . ,x p be the values of p covariates. According to the Cox regression model, the hazard function is given as follows 5,12