I am using Deep Recurrent Survival Machines (RDSM) model from auton-survival library for survival analysis with time-varying covariates. I am using a longitudinal survey dataset (Health and Retirement Study), where every respondent has variables from 13 waves at max.
I first tried to understand how RDSM works (link to the paper http://proceedings.mlr.press/v146/nagpal21a.html). From what I understood, they assume that a time to event follows a mixture of parametric distributions (like Weibull, Lognormal). Then the shape and scale of the distributions are implemented as a function of the input covariates using RNNs.
- My question is: how predictions are usually made in time-varying survival analysis?
In my case, RDSM treats each sample at every time point separately. What I mean is, for example if I have repondent's covariates from 2 waves, predictions at wave 1 and wave 2 are made separately, as if they come from different respondents. I am not sure whether this is correct. I think the model should know that the next set of covariates are coming from the same respondent. Or is this temporal relationship already captured while obtaining feature representations from RNN?
To be more clear, my test set is 4691, there are 13 time steps, and 206 covariates. I am expecting 4691 survival curves from my model, but it gives me 4691*13 = 60983 survival curves. Is this how it should be?
Any help or thought will be appreciated.