Bayesian latent class analysis when the reference test is imperfect
Bibliographic record
Abstract
Latent class analysis (LCA) has allowed epidemiologists to overcome the practical constraints faced by traditional diagnostic test evaluation methods, which require both a gold standard diagnostic test and ample numbers of appropriate reference samples. Over the past four decades, LCA methods have expanded to allow epidemiologists to evaluate diagnostic tests and estimate true prevalence using imperfect tests over a variety of complex data structures and scenarios, including during the emergence of novel infectious diseases. The objective of this review is to provide an overview of recent developments in LCA methods, as well as a practical guide to applying Bayesian LCA (BLCA) to the evaluation of diagnostic tests. Before conducting a BLCA, the suitability of BLCA for the pathogen of interest, the availability of appropriate samples, the number of diagnostic tests, and the structure of the data should be carefully considered. While formulating the model, the model's structure and specification of informative priors will affect the likelihood that useful inferences can be drawn. With the growing need for advanced analytical methods to evaluate diagnostic tests for newly emerging diseases, LCA is a promising field of research for both the veterinary and medical disciplines.
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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 | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".