Bibliographic record
Abstract
Relative risks (RRs) and prevalence ratios (PRs) are measures of association that are more intuitively interpretable than odds ratios (ORs). Many health science studies report OR estimates, however, even when their designs permit and study questions target RRs and/or PRs. This is, partially, attributable to the popularity and technical advantage (i.e. no restriction on the parameter space) of logistic regression for estimating ORs. To improve this practice, several biostatistical approaches for estimating RR/PR, adjusting for potential confounders, have been proposed. In this paper, we consider two RR/PR estimating methods: (1) the modification of log-binomial regression with the COPY method; and (2) an inverse-probability-of-treatment-weighted (IPTW) log-binomial regression we newly propose. For the COPY method, we rigorously establish the existence and uniqueness of the maximum-likelihood estimator, provided certain degeneracies in the data do not occur. Moreover, the global maximum of the COPY-modified likelihood is shown to occur at an interior point of the restricted parameter space. This result explains why the COPY method avoids convergence problems of log-binomial models frequently. For the IPTW estimator, we show that its simple procedure results in standardized estimates of RR/PR, and discuss its potential challenges, extensions, and an improvement through propensity-score-based grouping of observations. Furthermore, we compare the performances of four RR/PR estimation methods, including the COPY method and IPTW log-binomial regression, on simulated data. We demonstrate a lack of robustness of the COPY method against misspecification of the true relationship between binary outcome and explanatory variables, and show robustness of the IPTW approach in this regard.
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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 | 0.109 | 0.392 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".