B1434
Title: Testing for sufficient follow-up in survival data with immunes
Authors: Tsz Pang Yuen - Korteweg-de Vries Institute for Mathematics, University of Amsterdam (Netherlands) [presenting]
Eni Musta - University of Amsterdam (Netherlands)
Abstract: In order to estimate the proportion of 'immune' or 'cured' subjects who will never experience failure, a sufficiently long follow-up period is required. Several statistical tests have been proposed in the literature for assessing the assumption of sufficient follow-up. However, they have not been satisfactory for practical purposes due to their conservative behaviour or underlying parametric assumptions. A novel method is proposed for testing sufficient follow-up under general nonparametric assumptions. This approach differs from existing methods. The hypotheses are formulated in a broader context, eliminating the requirement for event times of interest to have compact support. Instead, the notion of sufficient follow-up is characterized by the quantiles of the distribution. The underlying assumption for the proposed method is that the event times have a non-increasing density function, which can also be relaxed to an unimodal density. The test is based on a shape-constrained density estimator such as the Grenander or the kernel-smoothed Grenander estimator. The performance of the test is investigated through a simulation study, and the method is illustrated on data from cancer clinical trials.
