= D. Y. Lin
{c}
<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.<Nonparametric analysis of recurrent events and death by D. Ghosh and D. Y. Lin>[Publisher byline and abstract; Biometrics 56(2), 554–562]{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.
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