Speaker: Martin Berg Johansen
Abstract: In event history analysis, periodic examinations may lead to event times which are known only to lie within a certain time interval. This can occur when a patient group is followed by routine controls or when a screening for a disease, for example cancer screenings, is performed in a population. In such cases, event times are said to be interval censored. Although a common phenomenon, interval censoring can be notoriously difficult to deal with analytically and is often unjustly ignored in applications.
Pseudo-observations have been proposed by Andersen, Klein and Rosthøj1 and can be used to formulate regression models using a non-parametric estimator of the marginal survival function, or cumulative incidence function in the presence of competing risks, by applying a generalized linear model to the derived pseudo-observations. Pseudo-observations enable routine regression analysis of clinically relevant effect measures, including risk ratios, risk differences, and the restricted mean survival time. The theory behind pseudo-observation methods does not, however, cover existing non-parametric estimators of the survival function based on interval censored data.
We aimed to construct a simple method for generating pseudo-observations in the context of interval censored event times. To this end, we used the approach of Royston and Parmar2 as a preliminary step to constructing a flexible spline-based parametric estimator of the marginal survival function.
References
1) Andersen, Biometrika 2003; 90:15–27
2) Royston, Stat Med. 2002; 21(15):2175–2197