A Bayesian multivariate approach to estimating the prevalence of a superordinate category of disorders
Bibliographic record
Abstract
OBJECTIVE: Epidemiological research plays an important role in public health, facilitated by the meta-analytic aggregation of epidemiological trials into a single, more powerful estimate. This form of aggregation is complicated when estimating the prevalence of a superordinate category of disorders (e.g., "any anxiety disorder," "any cardiac disorder") because epidemiological studies rarely include all of the disorders selected to define the superordinate category. In this paper, we suggest that estimating the prevalence of a superordinate category based on studies with differing operationalization of that category (in the form of different disorders measured) is both common and ill-advised. Our objective is to provide a better approach. METHODS: We propose a multivariate method using individual disorder prevalences to produce a fully Bayesian estimate of the probability of having one or more of those disorders. We validate this approach using a recent case study and parameter recovery simulations. RESULTS: Our approach produced less biased and more reliable estimates than other common approaches, which were at times highly biased. CONCLUSION: Although our approach entails additional effort (e.g., contacting authors for individual participant data), the improved accuracy of the prevalence estimates obtained is significant and therefore recommended.
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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 | 0.034 | 0.095 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".