I am learning survival analysis in R, especially the Cox proportional hazard model. I read a paper talking about using 80% of the sample as training set and 20% of sample as test set.

As quoted 

> On the training set, we first performed a pre-selection step to keep the top significant features correlated with overall survival (univariate Cox model, likelihood ratio test, P< 0.05). ...We used two computational methods to train the models: (i) Cox: the Cox proportional hazards model with LASSO for feature selection ...We then applied the models thereby obtained to the test set for prediction, and calculated the C-index using the R package survcomp.

I do not know how they actually did to apply the models from Cox model to the test set. I mean, for the training set, I can simply perform a coxph function. But the returned results are "coef,exp(coef),se(coef)),z,p" and likelihood ratio test p-value. How can I treat this as a model and use it on the 20% test set data?
You can try to do something like this:

```
# Derive model in the training data (after feature selection - I believe that in the paper you mentioned they use LASSO: R has a good package for this: glmnet)
cox_model = coxph(Surv(training_data$Survival,training_data$Status) ~ ., data=training_data)

# Create survival estimates on validation data
pred_validation = predict (cox_model, newdata = validation_data)

# Determine concordance
cindex_validation = concordance.index (pred_validation, surv.time = validation_data$Survival,
                                       surv.event=validation_data$Status, method = "noether")
```

See more [here][1]

Hope it helps

 [1]: http://stats.stackexchange.com/questions/48298/computing-c-index-for-an-external-validation-of-a-cox-ph-model-with-r