Reducing bias due to misclassified exposures using instrumental variables
Bibliographic record
Abstract
Abstract Exposures are often misclassified in observational studies. Any analysis that does not make proper adjustments for misclassification may result in biased estimates of model parameters, resulting in distorted inference. Settings where a multicategory exposure variable has more than two nominal categories or where no validation data are available to assess misclassification probabilities are common in practice but seldom considered in the literature. This article presents a novel method of analyzing cohort data with a misclassified, multicategory exposure variable and a binary response variable that uses instrumental variables in lieu of a validation dataset. First, a sufficient condition is obtained for model identifiability. Then, methods for model estimation and inference are proposed after adopting a sufficient condition for identifiability. We consider a variational Bayesian inference procedure aided by automatic differentiation along with Markov chain Monte Carlo‐based computation. Operating characteristics of the proposed methods are assessed through simulation studies. For the purpose of illustration, the proposed Bayesian methods are applied to the U.S. breast cancer mortality data sampled from the Surveillance Epidemiology and End Results database, where reported treatment therapy is the misclassified multicategory exposure variable.
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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 | 0.044 | 0.158 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".