Title: Survival Trees for Interval-Censored Survival data,
Abstract: Interval-censored data, in which the event time is only known to lie in some
time interval, arise commonly in practice; for example, in a medical study in
which patients visit clinics or hospitals at pre-scheduled times, and the
events of interest occur between visits. Such data are appropriately analyzed
using methods that account for this uncertainty in event time measurement. In
this paper we propose a survival tree method for interval-censored data based
on the conditional inference framework. Using Monte Carlo simulations we find
that the tree is effective in uncovering underlying tree structure, performs
similarly to an interval-censored Cox proportional hazards model fit when the
true relationship is linear, and performs at least as well as (and in the
presence of right-censoring outperforms) the Cox model when the true
relationship is not linear. Further, the interval-censored tree outperforms
survival trees based on imputing the event time as an endpoint or the midpoint
of the censoring interval. We illustrate the application of the method on tooth
emergence data.