Title: Statistical methods for doubly truncated data
Authors: Carla Moreira - University of Minho (Portugal) [presenting]
Abstract: Truncation is a well-known phenomenon that may be present in observational studies of time-to-event data. For example, when the sample restricts to those individuals with events falling between two particular dates, they are subject to selection bias due to the simultaneous presence of left and right truncation, also known as interval sampling, leading to a double truncation. When time-to-event data is doubly truncated, the sampling information includes the variable of interest $X$ and left-truncation and right-truncation variables $U$ and $V$, but the observable population reduces to those individuals for which the variable of interest lies between left-truncation and right-truncation variables. In this case, both large and small values of $X$ are observed in principle with a relatively small probability. The problem of estimating the distribution of $X$ and other related curves, such as kernel density and kernel hazard functions, using nonparametric and semiparametric approaches, from a set of iid triplets with the distribution of $(X, U, V)$ given the double truncation restriction will be presented. Several scenarios will be reported where the effect of ignoring double truncation appears in practice. Possible limitations of the nonparametric and semiparametric estimators will be discussed.