Estimating a time-to-event distribution from right-truncated data in an epidemic

Shaun R Seaman, Anne Presanis, Christopher Jackson

OAI: oai:www.repository.cam.ac.uk:1810/340941 • DOI: 10.17863/CAM.88376

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.

Coronavirus disease cox regression failure time identifiability relative efficiency right-truncation Survival Analysis Bias covid-19 Data Interpretation Statistical Humans Models Statistical Survival Analysis