6th CEN Conference

Session

Missing data 1

21 May 2026, 13:45

Room 13 A

Conveners

Missing data 1

• Łukasz Smaga (Adam Mickiewicz University)

153. Prediction in the presence of missing values. Are there credible alternatives to imputation-based use of the predictive density?

Bart Mertens (Leiden University Medical Centre)

21/05/2026, 13:45

oral presentation

Prediction in the presence of missing values is a complex and still poorly understood problem, particularly when future records also contain missing values.
Mertens, et al. (2020) demonstrate that with non-linear models (such as logistic regression or Cox survival) and when using imputations, averaging of multiple predictions obtained from distinct models fitted on imputed data should be...

198. Impact of imputation on individual cough alert system with incomplete baseline monitoring

Dörte Huscher (Institute of Biometry and Clinical Epidemiology, and Berlin Institute of Health, Charité - Universitätsmedizin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin)

21/05/2026, 14:03

oral presentation

In a prospective study of patients with muco-obstructive lung disease, aiming to develop a cough alert system based on nocturnal cough monitoring, to identify patient-individual thresholds at which cough frequency exceeds normal variability, so far 92 of intended 220 patients were included.
From den Brinker et al. in a study with 30 COPD patients it is known, that the day-to-day variation of...

79. The performance paradox: understanding the discrepancy between the performance of imputation approaches for survival models with missing covariates in simulation studies and real data

Edouard F. Bonneville (LMU Munich, and Munich Center for Machine Learning)

21/05/2026, 14:21

oral presentation

Multiple imputation (MI) continues to be a popular approach to deal with missing at-random covariate data. For MI to perform well, it is advisable to ensure that the imputation model for a given covariate does not make conflicting assumptions with substantive/analysis model. In the case of substantive models that assume proportional hazards (e.g., the standard Cox model for a single...

14. Handling Missing Data in Life Science: A Comparative Study of Imputation Methods for Medical Data.

Maria Thurow (TU Dortmund University)

21/05/2026, 14:39

oral presentation

Handling of missing data is a crucial aspect when preparing data sets for further analyses in several research areas. Previous studies have shown that the choice of imputation method can have a high influence on subsequent analyses, especially in medical research, where missing values often occur due to study design or data collection challenges.
In this study, we conduct a comparative...

171. Evaluating the Impact of Missing Data Imputation Methods on Bias and Covariate Balance in Propensity Score Analysis: A Simulation Study

Saghar Garayemi (Augsburg University)

21/05/2026, 14:57

oral presentation

Missing covariate data is a significant source of bias in observational studies that use propensity score (PS) analysis to make causal inference. The accuracy of treatment effect estimation is determined not just by how missing data is handled, but also by the method used to calculate propensity scores. A variety of methods for handling missing covariate data in propensity score analyses have...