Tuesday, September 22, 3.15 pm (OBS! CHANGED TIME), 2026. Seminar in Statistics.

Dependent Censoring: An Ignored but Not Ignorable Problem in Survival Analysis
Ewa Wycinka, Department of Statistics, Faculty of Management, University of Gdańsk
Abstract: Survival analysis is the primary statistical framework for studying time-to-event data and has applications across medicine, epidemiology, engineering, economics, and computer science. A defining feature of such data is censoring, whereby the event time is only partially observed. Standard methods, including the Kaplan-Meier estimator and Cox proportional hazards model, rely on the assumption that the censoring mechanism is independent of the event process. Although routinely adopted, this assumption is often difficult to justify in practice and may be violated in many real-world settings.
The presentation begins with an overview of the fundamental concepts of survival analysis and its principal fields of application. It then examines censoring from four complementary perspectives, providing a structured framework for understanding the different mechanisms leading to incomplete observations. Particular attention is devoted to dependent censoring, in which the censoring process is associated with the underlying event time. Common sources of such dependence and their consequences for survival estimation, regression modeling, prediction, and statistical inference are discussed.
A central challenge is that dependent censoring is generally not testable from the observed data alone. Nevertheless, under certain conditions its presence may be identified or its plausibility assessed using study-specific knowledge, auxiliary information, external data sources, or explicit modelling assumptions. The opportunities and limitations of these approaches are examined.
The final part of the presentation reviews statistical methods designed to address dependent censoring, including inverse probability of censoring weighting, joint modelling, copula-based approaches, and sensitivity-analysis techniques. Emphasis is placed on the assumptions underlying these methods, their practical applicability, and the extent to which they can mitigate bias arising from informative censoring.

Location: Alan Turing .

Tuesday, October 20, 1.30 pm (NEW TIME), 2026. Seminar in Statistics.

TBA
Valentin Patilea, ENSAI
Abstract:
Location: Alan Turing .

Tuesday, November 3, 1.30 pm (NEW TIME), 2026. Seminar in Statistics.

TBA
Sebastian Zeng, Department of mathematics and physics, Linnæus University
Abstract:
Location: Alan Turing .

Tuesday, December 1, 1.30 pm (NEW TIME), 2026. Seminar in Statistics.

TBA
Maria Karlsson, Umeå School of Business, Economics and Statistics, Umeå University
Abstract:
Location: Alan Turing .