  The dynamics of a population exhibiting exponential growth can be modelled as
a birth-death process, which naturally captures the stochastic variation in
population size over time. In this article, we consider a supercritical
birth-death process, started at a random time in the past, and conditioned to
have n sampled individuals at the present. The genealogy of individuals sampled
at the present time is then described by the reversed reconstructed process
(RRP), which traces the ancestry of the sample backwards from the present. We
show that a simple, analytic, time rescaling of the RRP provides a
straightforward way to derive its inter-event times. The same rescaling
characterises other distributions underlying this process, obtained elsewhere
in the literature via more cumbersome calculations. We also consider the case
of incomplete sampling of the population, in which each leaf of the genealogy
is retained with an independent Bernoulli trial with probability $\psi$, and we
show that corresponding results for Bernoulli-sampled RRPs can be derived using
time rescaling, for any values of the underlying parameters. A central result
is the derivation of a scaling limit as $\psi$ approaches 0, corresponding to
the underlying population growing to infinity, using the time rescaling
formalism. We show that in this setting, after a linear time rescaling, the
event times are the order statistics of $n$ logistic random variables with mode
$\log(1/\psi)$; moreover, we show that the inter-event times are approximately
exponentially distributed.
