Question: R use models from survival analysis and do prediction
2
6.2 years ago by
hdy130
United States
hdy130 wrote:

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 likelood ratio test p-value. How can I treat this as a model and use it on the 20% test set data?

modified 4.7 years ago by openabstract0 • written 6.2 years ago by hdy130

could you give the reference, please

1

paper name "Assessing the clinical utility of cancer genomic and proteomic data across tumor types" is on nature biotechnology. Thanks!

5
6.2 years ago by
sarajbc50
sarajbc50 wrote:

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")

Hope it helps