ArticleESMO open2025
Informative censoring in externally controlled clinical trials: a potential source of bias.
Article in ESMO open, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Strategies to boost statistical efficiency in randomized oncology trials with primary time-to-event endpoints.Statistical methods in medical research · 2025Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
backgroundCancer researchers frequently consider the use of single-arm and randomized controlled clinical trial designs that leverage external data. The literature has reported extensively on how the use of external data can introduce bias through a variety of distortion mechanisms. In this article, we focus on a distortion mechanism that is often overlooked: informative censoring. Informative censoring arises when there is statistical dependence between patients' censoring times and survival times. MATERIALS AND
methodsWe used simulations to investigate how informative censoring of external controls (ECs) can influence the results of cancer clinical trials. Our simulations included the following: (i) model-based replicates of clinical trials and in silico glioblastoma trials obtained by resampling patients from completed phase III trials; (ii) single-arm and randomized controlled cancer clinical trial designs; and (iii) different types of informative censoring, with positive or negative associations between censoring times and survival times.
resultsOur simulations showed that informative censoring of EC data can bias cancer clinical trial results. The direction of the bias depends on the censoring mechanism. Similarly, informative censoring can inflate or reduce type I error and power.
conclusionsSelection of EC data and the decision to leverage these data in the analysis of clinical trials should account for the risk of bias due to informative censoring. Analyses to detect informative censoring are recommended when the clinical trial design involves external data.
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