Predict Survival using RMS package in R? - r

Question

I am using the function survest in the RMS package to generate survival probabilities. I want to be able to take a subset of my data and pass it through survest. I have developed a for loop that does this. This runs and outputs survival probabilities for each set of predictors.
for (i in 1:nrow(df)){
row <- df[i,]
print(row)
surv=survest(fit, row, times=365)
print(surv)
}
My first question is whether there is a way to use survest to predict median survival rather than having to specify a specific time frame, or alternatively is there a better function to use?
Secondly,I want to be able to predict survival using only four of the five predictors of my cox model, for example (as below), while I understand this will be less accurate is it possible to do this using survest?
survest(fit, expand.grid(Years.to.birth =NA, Tumor.stage=1, Date=2000,
Somatic.mutations=2, ttype="brca"), times=300)

Answer 1

To get median survival time, use the Quantile function generator, or the summary.survfit function in the survival package. The function created by Quantile can be evaluated for the 0.5 quantile. It is a function of the linear predict. You'll need to use the predict function on the subset of observations to get the linear predictor value to pass to compute the median.
For your other two questions, survest needs to use the full model you fitted (all the variables). You would need to use multiple imputation if a variable is not available, or a quick approximate refit to the model a la fastbw.

Answer 2

We are trying to do something similar with the missing data.
While MI is a good idea, a simpler idea for a single missing variable is to run the prediction multiple times, and replace the missing variable with a value sampled at random distribution of the missing variable.
E.g. If we have x1, x2 and x3 as predictors, and we want to model when x3 is missing, we run predictions using x1 and x2 and take_random_sample_from(x3), and then averaging the survival times over all of the results.
The problem with reformulating the model (e.g. in this case re-modelling so we only consider x1 and x2) is that it doesn't let you explore the impact of x3 explicitly.
For simple cases this should work - it is essentially averaging the survival prediction for a large range of x3, and therefore makes x3 relatively uninformative.
HTH,
Matt