ArticleJAMIA open2024
smdi: an R package to perform structural missing data investigations on partially observed confounders in real-world evidence studies.
Article in JAMIA open, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Risk of Hyperkalemia in Patients with Heart Failure Treated with Spironolactone in Combination with Sacubitril/Valsartan vs. Renin-Angiotensin System Inhibitors.Clinical pharmacology and therapeutics · 2026Article
- Comparative effectiveness of CDK4/6 inhibitors in metastatic breast cancer: using the target trial emulation framework to investigate overall survival in routine care.Breast cancer research and treatment · 2026Article
- Use of Clinical Notes to Assess Neuropsychiatric Events After Montelukast Initiation.JAMA network open · 2026Article
- High-dimensional multiple imputation for partially observed confounders including natural language processing-derived auxiliary covariates.American journal of epidemiology · 2026Article
- Article
- Strengthening inferential studies in the FDA Sentinel initiative: results from a methodological demonstration project.NPJ digital medicine · 2025Article
- Clustering-Informed Shared-Structure Variational Autoencoder for Missing Data Imputation in Large-Scale Healthcare Data.Statistics in medicine · 2025Article
- Analyzing missingness patterns in real-world data using the SMDI toolkit: application to a linked EHR-claims pharmacoepidemiology study.BMC medical research methodology · 2024Article
- smdi: an R package to perform structural missing data investigations on partially observed confounders in real-world evidence studies.JAMIA open · 2024Article
- A Principled Approach to Characterize and Analyze Partially Observed Confounder Data from Electronic Health Records.Clinical epidemiology · 2024Article
Corrections and comments
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Authors and funding
14 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objectives: Partially observed confounder data pose a major challenge in statistical analyses aimed to inform causal inference using electronic health records (EHRs). While analytic approaches such as imputation are available, assumptions on underlying missingness patterns and mechanisms must be verified. We aimed to develop a toolkit to streamline missing data diagnostics to guide choice of analytic approaches based on meeting necessary assumptions. Materials and methods: We developed the smdi (structural missing data investigations) R package based on results of a previous simulation study which considered structural assumptions of common missing data mechanisms in EHR. Results: smdi enables users to run principled missing data investigations on partially observed confounders and implement functions to visualize, describe, and infer potential missingness patterns and mechanisms based on observed data. Conclusions: The smdi R package is freely available on CRAN and can provide valuable insights into underlying missingness patterns and mechanisms and thereby help improve the robustness of real-world evidence studies.
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