Selecting the Best Method among Several: Bayesian and Classical Data Analyses Comparison in a Complex Microbiological Validation Setting
Bibliographic record
Abstract
Statistical procedures to compare new methods to gold standards, after validation of microbiological food methods, exist. However, evaluation of the best microbiological detection method among several is more challenging; there is little precedent in scientific literature. Our work compares Bayesian hierarchical (BH), Bayesian logistic Anova-like (BL-AL) and Classical logistic (CL) models using an original validation study, based on Health Canada’s Microbiological Methods Committee guidelines.The validation study design includes 6 microbiological methods, 13 food panels of 20 samples each, and 10 laboratories, and theoretically generates 780 sub-groups, reduced to 198 after quality review. In classical statistics this would lead to 231 null hypothesis tests, requiring significance level (α) correction for multiple comparisons. Assuming non-informative priors, BH and BL-AL estimations include meaningful parameters (e.g. method detection rate (DR), DR differences between methods, etc.), reallocate their credibility based on observed data, and provide posterior distributions and 95% high credibility intervals (HCI). Joining HCI with pre-defined Regions of Practical Equivalence (ROPEs) allows for null hypotheses decision making: rejection, acceptance, or no decision.BH and BL-AL give similar results and posteriors. Posteriors’ modes are nearly identical to best-estimates computed with CL. Statistical conclusions will be similar between BH, BL-AL and CL if, and only if, significance level (α) is corrected for multiple comparisons. Nevertheless only Bayesian allows null hypotheses acceptance and a clean ranking of the 6 microbiological methods using posterior densities within, below, and above ROPE limits.Using flat priors, it isn’t surprising to find similarities between Bayesian and Classical methods. However, while avoiding classical paradigm misinterpretation issues, the Bayesian framework provides informative and meaningful results with no multiple comparison issues.
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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 | 0.152 | 0.295 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".