Discretizing Continuous Event Time Data.

Although data may capture continuous event times or event times with high resolution (e.g., day), some statistical analyses require the discretization of time into intervals and assigning each event (i.e., outcome or loss to follow-up [LTFU]) to the start or end of an interval. First, using a simulated example, we showed that outcomes should be assigned to the end of the interval. Next, we considered four approaches for assigning LTFU events in a simulated example and in 20 real datasets. Comparing the resulting cumulative risk curves with the curve using continuous time, one approach always had the least error: assigning LTFU to the start or end of the interval, depending on which was closest to the continuous event time. This approach was superior to always censoring at the beginning or end of the interval.