Multiple Imputation Approaches for Missing Time-to-Event Outcomes with Informative Censoring: Practical Considerations from a Simulation Study Based on Real Data

Multiple Imputation Approaches for Missing Time-to-Event Outcomes with Informative Censoring: Practical Considerations from a Simulation Study Based on Real Data

Missing outcomes data represent a common threat to the validity and robustness of clinical trials with time-to-event outcomes. Recent extensions of multiple imputations (MI), namely controlled-MI, have been introduced as a viable approach for sensitivity analysis in the presence of informative cens...

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Bibliographic Details
Main Authors: Andrea Bellavia, Min Guo, Sabina Murphy
Format: Article
Language:English
Published: Milano University Press 2025-03-01
Series:Epidemiology, Biostatistics and Public Health
Subjects:
missing data
multiple imputations
survival analysis
informative censoring
Online Access:https://riviste.unimi.it/index.php/ebph/article/view/28145
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https://riviste.unimi.it/index.php/ebph/article/view/28145

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