I have a diagnostic test performed on 100 participants at baseline. I then follow up these participants for variable periods of time and have data regarding survival.
I have used a Cox regression model to calculate a hazard ratio, and used the Mantel-cox log rank to test whether a positive test predicts death.
I would like to calculate time specific version of sensitivity and specificity of the test. I have been using the 'survivalROC' package in R to do this.
library(survivalROC) num_subj = NROW(data) survivalROC(Stime = data$survival_time, #time till censoring marker = data$positive_test, #1 for positive, 0 for negative status = data$death, #whether subject dead or alive predict.time = 2, #cutoff time span = 0.25*num_subjects^(-0.20))
From this I get 'True positive' and 'False positive values'
If I then run the same code but this time with
marker = data$negative_test, #1for negative, 0 for positive
I will get different True/false positive values.
My question is: can I put these 4 numbers in a 2x2 table (the values from the marker = data$negative_test used as the values for true and false negative) and calculate sensitivity and specificity in the usual manner (e.g. sens = TP/(TP+FN))?
Or please let me know if this whole approach is misguided and there are superior alternatives.