Bias from a missing covariate in the analysis of diagnostic test data in the absence of a gold-standard
Bibliographic record
Abstract
Covariates that influence the sensitivity and/or specificity of different diagnostic tests can create correlations between these tests, conditional on disease status. Thus, ignoring such covariates in a latent class analysis of imperfect tests would amount to ignoring conditional dependence, potentially leading to biased estimates of the prevalence of the condition under study and the accuracies of the tests. In the case of a dichotomous covariate affecting two imperfect tests, we derive an expression showing that the conditional covariance is a function of the product of the change in test sensitivity (or specificity) within subgroups defined by the covariate. For a uniformly or normally distributed continuous covariate, similar results are obtained numerically. Using series of simulated datasets, we study whether in the absence of covariate, unbiased estimates may be obtained by fitting a latent class model that allows for conditional dependence. We found that bias induced by ignoring the dependence and using a conditional independence model is not large in most cases. In cases where bias is present, a conditional dependence model, which places no constraints on the covariance between the tests, works well in adjusting for all three types of missing covariates. Our methods are applied to diagnostic testing data for the detection of tuberculosis which varies by the covariate HIV status.
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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 | 0.106 | 0.298 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| 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".