学术报告（钱静 2024.7.1）

摘要：In observational cohort studies with complex sampling schemes, truncation arises when the time to event of interest is observed only when it falls below or exceeds another random time, i.e., the truncation time. In more complex settings, observation may require a particular ordering of event times; we  refer to this as sequential truncation. We propose nonparametric and semiparametric maximum likelihood estimators for the distribution of the event time of interest in the presence of sequential truncation, under two truncation models. We develop methods for regression modeling in this complex setting using the tool of pseudo-observations. We evaluate our approach in simulation studies and in application to an Alzheimer's cohort study.