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Preface
In our data analysis we do some univariate analysis before proceeding to models. In survival analysis it is highly recommended to look at the Kaplan-Meier curves for all the categorical predictors. This will provide insight into the shape of the survival function for each group and give an idea of whether or not the groups are proportional. We also consider the tests of equality across strata to explore whether or not to include the predictor in the final model. For the categorical variables such as marital, eservice, plusservice and totalservice we use the log-rank test of equality across strata which is a non-parametric test. For the continuous variables such as age, address, income, education, employment, and reside we use a univariate Cox proportional hazard regression which is a semi-parametric model.
Univariate analysis
We consider the Chi-squared test for age, address, income, education, and employ. All the variables have p-values of 0.0000 thus age, address, income, education, and employ are the potential candidate for the final model since the p-value is less than our cut-off value of 0.2. But we get different result in case of reside. We consider the Chi-squared test for reside which has a p-value of 0.5413 thus reside is not a potential candidate for the final model since the p-value is more than our cut-off value of 0.2.
The log-rank test of equality across strata for the predictor marital has a p-value of 0.0136, thus marital will be included as a potential candidate for the final model because this p-value is still less than our cut-off of 0.2. From the graph (exhibit 1), we see that the survival function for each group of married or unmarried people are not perfectly parallel but separate except at the very beginning and at the very end.
[pic] [pic]
Exhibit 1: Kaplan-Meier Survival estimates for marital Exhibit 2:

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