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High-dimensional multiple imputation for partially observed confounders including natural language processing-derived
Janick Weberpals1, Pamela A Shaw2, Kueiyu Joshua Lin1
1Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States.
American Journal of Epidemiology
|January 23, 2025
Summary
High-dimensional multiple imputation (MI) using auxiliary covariates (AC) can reduce bias in studies with missing confounders. Combining structured and NLP-derived AC improved efficiency and bias in simulations.
Area of Science:
- Biostatistics
- Health Informatics
- Epidemiology
Background:
- Auxiliary covariates (AC) can enhance multiple imputation (MI) models.
- The performance of AC in high-dimensional data, especially with partially observed confounders, requires further investigation.
- Natural Language Processing (NLP) offers novel ways to derive AC from unstructured data.
Purpose of the Study:
- To develop and compare high-dimensional MI (HDMI) methods using structured and NLP-derived AC.
- To evaluate HDMI performance in simulated cohorts with partially observed confounders, specifically focusing on acute kidney injury studies.
- To assess the impact of omitting key confounders (e.g., atrial fibrillation) on imputation and treatment effect estimation.
Main Methods:
- A plasmode simulation was conducted with 100 cohorts, simulating acute kidney injury outcomes and null treatment effects.
- Creatinine lab values and atrial fibrillation (AFib) were included as confounders, with missingness imposed on creatinine.
- High-dimensional MI (HDMI) covariates were derived from structured (claims) and NLP-derived features, selected using LASSO for MI and propensity score models.
Main Results:
- HDMI using claims data demonstrated the lowest bias (0.072).
- Combining claims data with NLP-derived sentence embeddings improved efficiency (RMSE=0.173) and achieved 94% coverage.
- NLP-derived AC alone did not outperform baseline MI methods.
- Complete case analysis and baseline imputation showed higher bias compared to HDMI methods.
Conclusions:
- High-dimensional multiple imputation (HDMI) with auxiliary covariates can effectively decrease bias in studies where confounder missingness is related to unobserved factors.
- The integration of structured data (claims) and NLP-derived features offers a promising approach for improving the accuracy and efficiency of imputation in complex, high-dimensional datasets.
- Careful selection and derivation of auxiliary covariates are crucial for successful application of HDMI methods in observational health research.
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