title: Prediction Error Curves - Brier score
body: <p>I have a set of patients which are divided in a training and validation set. I would like to evaluate the performance of several parameters for stratifying overall survival. I therefore use the pec package (<a href="https://cran.r-project.org/web/packages/pec/pec.pdf" rel="nofollow">https://cran.r-project.org/web/packages/pec/pec.pdf</a>) to generate prediction error curves and the integrated brier score (IBS). The question that I have is whether there is a way to determine if the IBS of Model_1 is significantly lower as compared to Model_2 or the reference model?</p>
  
  <p>Thanks for your help!</p>
  
  <pre><code>models &lt;- list("Model_1" = coxph(Surv(OS, OS_event)~ var_1+var_2, data=df_train),
                 "Model_2" = coxph(Surv(OS, OS_event) ~ var_3+var_4, data=df_train))
  
  
  brier &lt;- pec(object=models,
               data=df_validate,
               formula=Surv(OS, OS_event)~age,
               exact=TRUE,
               cens.model="marginal",
               traindata = df_train)
  plot(brier)
  print(brier)
  </code></pre>