ArticleStatistics in medicine2026
A Cautionary Note for Plasmode Simulation Studies in the Setting of Causal Inference.
Article in Statistics in medicine, 2026. 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
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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.
- Tuning LASSO Models for Propensity Score Weighting and Using Synthetic Negative Control Exposures for Residual Bias Detection.Statistics in medicine · 2026Article
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Authors and funding
8 authors.
Funding
Abstract
Plasmode simulation has become an important tool for evaluating operating characteristics of different statistical methods in complex settings, such as pharmacoepidemiological studies of treatment effectiveness using electronic health records (EHR) data. These studies provide insight into how estimator performance is impacted by challenges including rare events, small sample size and so forth that can indicate which among a set of methods performs best in a real-world dataset. Plasmode simulation combines data resampled from a real-world dataset with data from a probabilistic model to generate a known truth for an estimand in realistic data. There are different potential plasmode strategies currently in use. We compare two popular plasmode simulation frameworks for the evaluation of methods estimating a point treatment estimand. We provide numerical evidence and a theoretical result that shows one of these frameworks can cause certain estimators to incorrectly appear overly biased. Detailed simulation studies using both model-generated and real-world EHR data demonstrate these pitfalls remain at large sample sizes and when analyzing data from a randomized controlled trial. We conclude with guidance for the choice of a plasmode framework that maintains good theoretical properties to allow a fair evaluation of statistical methods while also maintaining the desired similarity to real data.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.