Time-dependent ROC / Time-dependent AUC(t)
timeROC/survivalROC; this tool does not yet output a CI.A plain ROC has no time dimension. For a prognostic marker/model, discrimination changes over follow-up. Time-dependent AUC(t) measures "at time t, can we separate those who have had the event from those who have not", handling censoring with IPCW. Computed locally in your browser; data are not uploaded.
① Input survival data
Each row: time status marker (status 1=event, 0=censored). Space/Tab/comma-separated.
How to use & methodology
How does time-dependent AUC differ from ordinary AUC?
Ordinary ROC/AUC targets a fixed binary outcome with no time dimension. In prognosis research, 'whether the event occurred' depends on which time point you look at, and there is censoring. Time-dependent AUC(t) evaluates discrimination separately at each time point and corrects censoring bias with IPCW.
Why does the AUC change over time?
A marker may discriminate high vs low risk well early on but lose discrimination later (or the reverse). The time-dependent AUC(t) curve shows this change in discrimination over follow-up.
How are cases/controls defined?
This tool uses the 'cumulative/dynamic' definition: at time t, those with event time ≤t and an event are cases, those with time >t are controls; those censored before t are handled indirectly via IPCW weighting.
Why is there no confidence interval?
The CI for time-dependent AUC needs the bootstrap or asymptotic variance and is sensitive to the censoring estimate, making it error-prone. To be safe this tool gives only point estimates; for CIs and more robust estimates, use R's timeROC/survivalROC packages.