I've used time-series forecasting (ETS, ARIMA, etc.) models but because of the depth of my data I'm exploring using the survival and related packages in R to forecast future period survival probabilities. For example, if we have 24 months of survival data, I'm interested in forecasting months 25-36 using the statistical parameters derived from analyzing months 1-24, using either Kaplan-Meier or a parametric distribution like Weibull. In the form of simulation with multiple simulation paths derived, as I've done with traditional time-series methods. I'm exploring alternatives to the traditional time-series methods because they seem overly-simplistic in light of depth of data I have (similar to what you see in the lung and kidney datasets that are part of the survival package). Please, does anyone know if this type of probabilistic future period forecasting is possible in survival analysis, and know of any simple examples on-line that I can start with?
One obvious solution, as you already alluded to is to use a parametric model (such as Weibull regression). Non-parametric/semi-parametric models like Kaplan-Meier or Cox do in their standard implementation not extrapolate beyond the largest observed event time. There's other implementations that more or less do the same thing, but can extrapolate, e.g. an exponential model with piecewise constant hazard rates can be very similar to Cox regression, but will extrapolate if you just assume the hazard rate of the last interval going forward (similarly, e.g. the
brms R package implements a kind of Cox model using M-splines for the baseline hazard function which I think should permit extrapolation).
E.g. if you use R, one obvious option is to penalize Weibull regression
fit <- survreg(Surv(time, has_event) ~ 1 + ridge( predictor1, predictor2, ... , theta=1, scale=T), dist = "weibull", data=mydata), where the ridge-penalty
theta could be picked using cross-validation. After fitting, the traditional shape parameter is given by
1/fit$scale and the scale parameter for a record
predict(object=fit, newdata=newdata, type="lp"), so that you get an estimated survival curve by plotting the Weibull CDF with these parameters, but that ignores uncertainty about the parameters. This gets a lot easier to deal with in the Bayesian version of Weibull regression, which is nicely supported in the
brms R package (if you want instead of individual patient time predictions to predict the parameters of the distribution, there's some nuances: see here).
Alternatively, a more machine learning oriented approach would be to e.g. use XGBoost with an accelerated failure time loss, but calibration tends to be poor. However, there's alternatives that try to fix this.
for discrete time survival problems, a simple logistic regression framework with calendar month as an input is straightforward to use. your data set should be in so called person-period format, where for each month the person survived until the previous month, you create a row indicating whether they survived the current month. You can then interact time with any of your other independent variables, or use spline interpolation for your time variable etc - ie use any of the standard input transformations you would use with linear/logistic regression.
then to predict in the future you predict probability of surviving each month given survived up till then ( from logistic regression output, just changing the input time)
and your simulation is just a markov chain using these probability outputs