Past exam of the mathematics course of the University of Cambridge / 2016 / iii / Paper 207 / 6

For a nonnegative continuous event time $T$, the survivor function is $F(t)=\mathbb{P}(T>t)$, and its probability density function isThe hazard function is the instantaneous event rate conditional on being event-free at that time:If the event is certain to occur in finite time, the density is proper: ${\int }_0^{\infty }f(t)dt=1$, so $F(t)\to 0$. Since $F(0)=1$ and $F^{\prime }=-hF$, the cumulative hazard function is $H(t)=-\log F(t)$. Therefore a certain eventual event requires unbounded integrated hazard:If the lifetime has a finite upper endpoint, the same divergence occurs as that endpoint is approached, and $H$ is interpreted as infinite beyond it.

This is administrative censoring. Under the usual assumption that entry calendar time is independent of the subsequent event time, it is an example of independent censoring, not informative censoring: the planned study closure, rather than a patient's prognosis, determines the available follow-up. Uniform entry by itself does not prove that assumption; if entry date is associated with prognosis, independence must instead be justified conditionally on the relevant covariates. The calculations below use independence.

For administrative censoring with uniform entry, let entry time $A\sim \mathrm{Uniform}({\tau }_a,{\tau }_b)$. Then $C={\tau }_c-A$ has the uniform distribution on $[\ell ,r]$, where $\ell ={\tau }_c-{\tau }_b$, $r={\tau }_c-{\tau }_a$, and $d=r-\ell ={\tau }_b-{\tau }_a$. Its density and survivor function areDividing density by survival and integrating gives the censoring hazard and integrated hazard:The hazard function is undefined once no uncensored individuals remain. As $t\uparrow r$, it diverges like $1/(r-t)$ and the cumulative hazard function diverges logarithmically. This reflects the certainty of administrative censoring by the longest possible follow-up time, not dependence of censoring on the event process.

An individual has neither died nor been censored by time $t$ exactly when $T>t$ and $C>t$. Under independent censoring, this has probability $F_T(t)G(t)$. Thus the probability of leaving observation by either route isThe two pieces agree at $t={\tau }_c-{\tau }_b$. The expression counts the first of death and censoring, so it does not double-count individuals who would eventually experience both.