A clinical prediction model has been published in the field of oncology. The prediction model is only available as a nomogram or web-calculator. No regression coefficients or else are reported. I have calculated the probability of the outcome using the web-calculator.

I have tried the following:

auc_cox ← as.numeric(timeROC::timeROC(

T = dat$time_surgery, # time from surgery to outcome

delta = dat$status, # survival status

marker = dat$pred_model, # predicted probability of the outcome [0-1]

times = 3,

cause = 1

)$AUC[2]) # 2nd value because first value is at t = 0

val_ests ← rms::val.surv(

est.surv = dat$pred_model,

S = Surv(time = dat$time_surgery, event = dat$status),

u = 5*365,

fun=function(p)log(-log(p))

)

plot(val_ests, xlab=“Expected Survival Probability”,ylab=“Observed Survival Probability”)

However, the results are quite strange.

Is this the correct approach?

I want to calculate Harrel’s C, Uno’s AUC and draw a calibration plot.

Which R functions can I use to externally validate this model using an independent dataset?

Thank you.