A cautionary note for plasmode simulation studies in the setting of causal inference

Pamela A Shaw; Susan Gruber; Brian D. Williamson; Rishi Desai; Susan M. Shortreed; Chloe Krakauer; Jennifer C. Nelson; Mark J. van der Laan

arXiv · 2504.11740

A cautionary note for plasmode simulation studies in the setting of causal inference

Pamela A Shaw·Susan Gruber·Brian D. Williamson·Rishi Desai·Susan M. Shortreed·Chloe Krakauer·Jennifer C. Nelson·Mark J. van der Laan

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Abstract

Plasmode simulation has become an important tool for evaluating the 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, etc., 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 synthetic data 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. We provide numerical evidence and a theoretical result, which shows that one of these frameworks can cause certain estimators to incorrectly appear overly biased with lower than nominal confidence interval coverage. Detailed simulation studies using both synthetic and real-world EHR data demonstrate that 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 simulation approach 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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Pamela A Shaw, Susan Gruber, Brian D. Williamson, Rishi Desai, Susan M. Shortreed, Chloe Krakauer, Jennifer C. Nelson, Mark J. van der Laan. 2025-04-16. A cautionary note for plasmode simulation studies in the setting of causal inference. https://arxiv.org/abs/2504.11740

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