Multiple imputation for systematically missing confounders within a distributed data drug safety network: A simulation study and real‐world example
Bibliographic record
Abstract
PURPOSE: In distributed data networks, some data sites may be systematically missing important confounders that are captured by other sites in the network (eg, body mass index [BMI]). Multiple imputation may help repair bias in these scenarios. However, multiple imputation has not been described for distributed data networks where data access restrictions prevent centralized analysis. METHODS: We conducted a simulation study and a real-world analysis using the UK's Clinical Practice Research Datalink to evaluate multiple imputation for confounders that are systematically missing from a subset of data sites in mock distributed data networks. The simulation study addressed univariate missing data, while the real-world analysis addressed multivariate missing data. Both studies were designed as retrospective cohort studies of the effect of current statin use on the risk of myocardial infarction among patients with newly treated type 2 diabetes. RESULTS: In our simulation study, multiple imputation repaired bias from missing BMI in all scenarios, with a median bias reduction of 118% in the default scenario. In our real-world study, the multiply imputed analysis (hazard ratio [HR]: 0.86; 95% confidence interval [CI], 0.69-1.08) was closer to the analysis that considered the true confounder values (HR: 0.85; 95% CI, 0.66-1.10) than the analysis that ignored them (HR: 0.93; 95% CI, 0.73-1.20). CONCLUSIONS: Multiple imputation adapted to distributed data settings is a feasible method to reduce bias from unmeasured but measurable confounders when at least one database contains the variables of interest. Further research is needed to evaluate its validity in real distributed data networks.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".