Abstract
Time-to-event data are right-truncated if only individuals who have experienced
the event by a certain time can be included in the sample. For example, we may
be interested in estimating the distribution of time from onset of disease
symptoms to death and only have data on individuals who have died. This may be
the case, for example, at the beginning of an epidemic. Right truncation causes
the distribution of times to event in the sample to be biased towards shorter
times compared to the population distribution, and appropriate statistical
methods should be used to account for this bias. This article is a review of
such methods, particularly in the context of an infectious disease epidemic,
like COVID-19. We consider methods for estimating the marginal time-to-event
distribution, and compare their efficiencies. (Non-)identifiability of the
distribution is an important issue with right-truncated data, particularly at
the beginning of an epidemic, and this is discussed in detail. We also review
methods for estimating the effects of covariates on the time to event. An
illustration of the application of many of these methods is provided, using data
on individuals who had died with coronavirus disease by 5 April 2020.
Collapse