cross validation output interpretation from survival analysis
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Entering edit mode
19 months ago
Palgrave ▴ 80

I have performed a cross validation using the errorest function, but am not sure how to interpret the output Brier score. Is there a way to visualise the cross validation? Any other suggestions how to perform an visualise the CV.

library(ipred)

df.t <- structure(list(time = c(1796, 1644.04166666667, 
606.041666666667, 1327.04166666667, 665, 2461, 1824, 1554.04166666667, 
601.958333333333, 1638.95833333333), status = c(0L, 
0L, 1L, 0L, 1L, 0L, 0L, 0L, 1L, 1L), Comb2 = c("Low", "Low", 
"High", "Low", "High", "Low", "Low", "High", "High", "Low")), row.names = c("1025", 
"1101", "1198", "1330", "1393", "1428", "1473", "1676", "175", 
"1754"), class = "data.frame")


err <- errorest(Surv(time, status) ~Comb2, data=df.t, model=survfit,
     predict=NULL, est.para=control.errorest(k=5))

out:

Call:
errorest.data.frame(formula = Surv(time, status) ~ Comb2, data = df.t.top, 
    model = survfit, predict = NULL, est.para = control.errorest(k = 5))

     5-fold cross-validation estimator of Brier's score

Brier's score:  0.2622
survival R coxph cross-validation • 527 views
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