ArticleNPJ digital medicine2025
Negative control-calibrated difference-in-difference analyses: addressing unmeasured confounding in RWD with application to racial/ethnic differences.
Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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.
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Who cites it
4 citing papers in PubMed.
- Counterfactual Dermatology With Generative Models.International journal of dermatology · 2026Article
- Discovering repurposable drugs for Alzheimer's disease and related dementias: target trial emulation using decentralised real-world data.EBioMedicine · 2026Article
- Simulation of covariate and concept drift in machine learning hospital admission prediction from emergency triage.NPJ digital medicine · 2026Article
- Post-acute dyslipidemia and abnormal BMI in children and adolescents with COVID-19: An EHR Cohort Study from the RECOVER Initiative.medRxiv : the preprint server for health sciences · 2025Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
Real-world data (RWD) from electronic health records and digital health databases present unique opportunities to study causal effects in healthcare. While Difference-in-Differences (DiD) analysis is widely used for such analyses, it can be biased when time-varying unmeasured confounding violates the parallel trends assumption. We propose a negative control-calibrated difference-in-difference (NC-DiD) approach that uses negative control outcomes (NCOs) both before and after the intervention to detect and adjust for such confounding. The method remains robust even with partially unreliable controls. In simulations, NC-DiD reduces bias, controls type-I error, and improves estimation accuracy. We applied NC-DiD to assess racial/ethnic disparities in post COVID-19 health outcomes using RWD emulated from pediatric 15,373 patients across eight children's hospitals. Results revealed worse long-term outcomes for minority groups compared to Non-Hispanic White patients. NC-DiD offers a robust framework for deriving reliable causal insights from digital health data, supporting evidence-based clinical decision-making and potentially improving patient outcomes.
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Registered trials
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