Using external data to incorporate unmeasured confounders: A plasmode simulation study comparing alternative approaches to impute body mass index in a study of the relationship between osteoarthritis and cardiovascular disease
Bibliographic record
Abstract
Background: Administrative databases do not contain Body Mass Index (BMI) informa- tion. In proportion-based imputation (PBI) technique, a BMI category is assigned to an individual according to the proportions observed in external survey data. Alternatively, BMI can be imputed using Multiple Imputation (MI). Objectives: To compare MI with PBI to impute BMI variable in osteoarthritis (OA)- cardiovascular disease (CVD) relationship. Research Design: plasmode simulation study. Subjects: used publicly available data from the Canadian Community Health Survey (CCHS) cycles 1.1, 2.1, and 3.1. Measures: BMI was set missing for everyone in the 500 simulated data created from CCHS 3.1 data. Dataset compiled from CCHS cycles 1.1 and 2.1 served as the external data (BMI observed). BMI missing in copies of simulated data was imputed using MI and PBI accessing observed BMI information in external data. After imputation, distribution of BMI variable and the adjusted odds ratio (aOR) estimated from multivariable logistic regression model were compared. Results: Compared to PBI, MI produced proportions of individuals closer to the known proportions across the BMI categories except for the overweight category. Considering the known aOR of 1.59 (1.36, 1.82), BMI imputed using MI introduced less bias in OA- CVD association compared to PBI, the aOR was 1.62 (1.39, 1.86) and 1.66 (1.41, 1.90), respectively. Conclusions: This is the first study to compare MI with PBI in the context of imputing BMI information that is not recorded at the database level. MI was superior to imputation method based on population-level proportions in imputing BMI missing for everyone in the simulated datasets.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".