Parametric Regression Analysis with Covariate Misclassification in Main Study/Validation Study Designs
Bibliographic record
Abstract
Measurement error and misclassification have long been a concern in many fields, including medicine, administrative health care data, epidemiology, and survey sampling. It is known that measurement error and misclassification may seriously degrade the quality of estimation and inference, and should be avoided whenever possible. However, in practice, it is inevitable that measurements contain error for a variety of reasons. It is thus necessary to develop statistical strategies to cope with this issue. Although many inference methods have been proposed in the literature to address mis-measurement effects, some important issues remain unexplored. Typically, it is generally unclear how the available methods may perform relative to each other. In this paper, capitalizing on the unique feature of discrete variables, we consider settings with misclassified binary covariates and investigate issues concerning covariate misclassification; our development parallels available strategies for handling measurement error in continuous covariates. Under a unified framework, we examine a number of valid inferential procedures for practical settings where a validation study, either internal or external, is available besides a main study. Furthermore, we compare the relative performance of these methods and make practical recommendations.
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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 | 0.412 | 0.640 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.008 | 0.007 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".