Dienstag, 28.04.2026, 12-13 Uhr in W9-109

Prof. Dr. Antonia Zapf
Institut für Medizinische Biometrie und Epidemiologie, Hamburg

Estimands and missing values in diagnostic accuracy studies

In 2020, the Estimand Framework was introduced in the appendix to the ICH E9 guideline for therapeutic trials. An estimand precisely defines the research question of interest, including the target treatment effect and strategies for handling intercurrent events that affect the interpretation or existence of measurements. Missing values represent a common challenge in clinical research, and multiple imputation is a frequently applied method to address this issue (Schaefer et al., 2026). However, when moving from therapeutic trials to diagnostic accuracy studies - where the objective is to assess the performance of a diagnostic test - statistical methodology remains less well developed. Recent work by Stahlmann et al. (2023) reviewed existing approaches for handling missing values in diagnostic studies and Stahlmann et al. (2025) and Juljugin et al. (2026) evaluated them in simulation studies. In addition, Fierenz et al. (2025) proposed an Estimand Framework tailored to diagnostic accuracy studies. Building on this work, the Estimand Framework has recently been evaluated in a simulation study in combination with different methods for addressing missing values. In this talk, I will introduce the concept of estimands in diagnostic accuracy studies and discuss approaches for handling missing values. Their practical relevance will be illustrated using an example study (Leutkens et al., 2025), and initial results from a simulation study will be presented (Stahlmann et al. 2026).

 

Dienstag, 12.05.2026, 12-13 Uhr in W9-109

Kurtulus Kidik
Universität Bielefeld

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Dienstag, 26.05.2026, 12-13 Uhr in W9-109

Prof. Dr. Göran Kauermann
Institut für Statistik der Ludwig-Maximilians-Universität München

Deriving multiple Information from pure Count Data – The Skellam Approach

We consider the scenario where we have count data at observational units, but we are interested in the quantities leading to the counts. Examples include the filling of bikes in racks of a bike network, but we are interested in the trips between stations of the network. Another example relates to the COVID-19 pandemic, where we observed the number of occupied beds in ICUs, but we are really interested in the number of incoming and outgoing patients. The talk demonstrates how the problem can be solved by relying on the Skellam distribution, which allows us to infer the number of incoming and outgoing patients from the occupancy in the ICUs. The talks goes a step beyond and approaches the additional question of whether we can also estimate the average length of stay of ICU patients. Hence, the task is to derive not only the number of incoming and outgoing patients from total net counts, but also to gain information on the duration time of patients on ICUs. We make use of a stochastic Expectation-Maximisation algorithm and additionally include exogenous information which are assumed to explain the intensity of inflow.

 

Dienstag, 09.06.2026, 12-13 Uhr in W9-109

Maya Vienken
Universität Bielefeld

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Dienstag, 23.06.2026, 12-13 Uhr in W9-109

Daniel Dzikowski
Technische Universität Dortmund

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Dienstag, 07.07.2026, 12-13 Uhr in W9-109

Dr. Alina Schenk
Institut für Medizinische Biometrie, Informatik und Epidemiologie der Universität Bonn

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Dienstag, 21.07.2026, 12-13 Uhr in W9-109

Dr. David Winkelmann
Universität Wien

Why bookmakers offer inefficient odds in football live-betting markets