Bayesian probability of agreement for comparing survival or reliability functions with parametric lifetime regression models
Bibliographic record
Abstract
In this article, we describe a quantitative approach for comparing the reliability or survival functions for two populations. The Bayesian probability of agreement (BPA) quantifies the similarity of the functions in regions of interest in the covariate space while accounting for a user-specified measure of what constitutes a practically important difference. The BPA method can be flexibly used for relationships with any number of covariates and for a variety of parametric models, including Weibull, lognormal and gamma regression. We provide an R Shiny app that allows practitioners to easily use the method without the need to implement the underlying computational details. Three examples from industrial and medical applications illustrate the implementation of the method as well as how to interpret the results from the analysis.
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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 | 0.136 | 0.404 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".