D. Y. Lin

D. Y. Lin coauthored the 2000 primary paper on nonparametric analysis of recurrent events and death with Debashis Ghosh. Its target is the marginal mean cumulative count of events in the presence of a terminal event, with estimation, covariance-based inference, simulation assessment and an application to a cancer trial. The publisher identifies Lin as the corresponding author.^[ref]

The statistical problem is that death ends the possibility of later events while preserving the events that occurred earlier. Let $C(t)$ count qualifying events up to time $t$, stopping at death. The population mean $m(t)=\mathbb{E}[C(t)]$ differs from an average calculated only among those alive at $t$.

An original two-person illustration makes the distinction explicit. In one complete history, an individual dies after zero events and the surviving individual has one; mean accumulated count per original individual is $1/2$. In another history, the deceased individual had four events before death while the survivor again has one; the mean is $5/2$. The survivor-only mean is one in both histories. Knowledge of the surviving group alone therefore cannot recover the original cohort’s accumulated burden.

A count at fixed time is a nonnegative random variable, but estimating its mean with incomplete observation is not merely an arithmetic exercise. Loss to follow-up hides later outcomes; an observed terminal event ends the defined process. Treating both identically can change the quantity being estimated. This is the connection between Lin’s documented recurrent-event work, survival analysis and the careful definition of a statistical endpoint.