  A core challenge in survival analysis is to model the distribution of
censored time-to-event data, where the event of interest may be a death,
failure, or occurrence of a specific event. Previous studies have showed that
ranking and maximum likelihood estimation (MLE)loss functions are widely-used
for survival analysis. However, ranking loss only focus on the ranking of
survival time and does not consider potential effect of samples for exact
survival time values. Furthermore, the MLE is unbounded and easily subject to
outliers (e.g., censored data), which may cause poor performance of modeling.
To handle the complexities of learning process and exploit valuable survival
time values, we propose a time-adaptive coordinate loss function, TripleSurv,
to achieve adaptive adjustments by introducing the differences in the survival
time between sample pairs into the ranking, which can encourage the model to
quantitatively rank relative risk of pairs, ultimately enhancing the accuracy
of predictions. Most importantly, the TripleSurv is proficient in quantifying
the relative risk between samples by ranking ordering of pairs, and consider
the time interval as a trade-off to calibrate the robustness of model over
sample distribution. Our TripleSurv is evaluated on three real-world survival
datasets and a public synthetic dataset. The results show that our method
outperforms the state-of-the-art methods and exhibits good model performance
and robustness on modeling various sophisticated data distributions with
different censor rates. Our code will be available upon acceptance.
