ArticleCPT: pharmacometrics & systems pharmacology2023
A systematic approach towards missing lab data in electronic health records: A case study in non-small cell lung cancer and multiple myeloma.
Article in CPT: pharmacometrics & systems pharmacology, 2023. 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.
- Determining a Likely Mechanism of Missingness in Repeated Measures Sleep Data From Wearable Fitness Trackers: Longitudinal Analysis.JMIR mHealth and uHealth · 2026Article
- Factor Structure and Measurement Invariance Across Gender of the Eating Disorder Examination Questionnaire-Short Form in Italian Workers.European journal of investigation in health, psychology and education · 2026Article
- Baseline Blood Eosinophil Count and 28-Day in-Hospital Mortality in ICU-Admitted Patients with AECOPD: A Cohort Study with External Validation.International journal of chronic obstructive pulmonary disease · 2026Article
- Analysis of Efficacy and Survival Prognosis for Astragalus Polysaccharide Injection as Adjuvant Therapy in Non - Small Cell Lung Cancer: A Propensity Score Matching - Based Study.Cancer management and research · 2025Article
- Exploring the Feasibility of a Bracketing Approach Utilizing Modeling for Development of Long-Acting Injectables for Regulatory Approval-A Case Study Using Levonorgestrel.Pharmaceuticals (Basel, Switzerland) · 2024Article
- smdi: an R package to perform structural missing data investigations on partially observed confounders in real-world evidence studies.JAMIA open · 2024Article
- Measurement error and bias in real-world oncology endpoints when constructing external control arms.Frontiers in drug safety and regulation · 2024Article
- A Principled Approach to Characterize and Analyze Partially Observed Confounder Data from Electronic Health Records.Clinical epidemiology · 2024Article
- A systematic approach towards missing lab data in electronic health records: A case study in non-small cell lung cancer and multiple myeloma.CPT: pharmacometrics & systems pharmacology · 2023Article
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Authors and funding
7 authors.
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Abstract
Real-world data derived from electronic health records often exhibit high levels of missingness in variables, such as laboratory results, presenting a challenge for statistical analyses. We developed a systematic workflow for gathering evidence of different missingness mechanisms and performing subsequent statistical analyses. We quantify evidence for missing completely at random (MCAR) or missing at random (MAR), mechanisms using Hotelling's multivariate t-test, and random forest classifiers, respectively. We further illustrate how to apply sensitivity analyses using the not at random fully conditional specification procedure to examine changes in parameter estimates under missing not at random (MNAR) mechanisms. In simulation studies, we validated these diagnostics and compared analytic bias under different mechanisms. To demonstrate the application of this workflow, we applied it to two exemplary case studies with an advanced non-small cell lung cancer and a multiple myeloma cohort derived from a real-world oncology database. Here, we found strong evidence against MCAR, and some evidence of MAR, implying that imputation approaches that attempt to predict missing values by fitting a model to observed data may be suitable for use. Sensitivity analyses did not suggest meaningful departures of our analytic results under potential MNAR mechanisms; these results were also in line with results reported in clinical trials.
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