Estimating Response-Maximized Decision Rules With Applications to Breastfeeding
Bibliographic record
Abstract
To estimate the sequence of actions that optimizes response in a longitudinal setting, it is important to study the actions as part of a set of decision rules rather than in a series of single-action comparisons. We take as our motivating example the estimation of the set of decision rules for the duration of breastfeeding with a view to maximizing infant growth. Breastfeeding has many well-recognized beneficial effects on health and development. However observational evidence has suggested that breastfeeding is associated with reduced infant growth, although the long-term consequences for stature and adiposity remain controversial. The Promotion of Breastfeeding Intervention Trial (PROBIT) recruited 17,046 women in which hospitals and their affiliated polyclinics in Belarus were randomized to a breastfeeding promotion intervention or to standard care. In this article, we propose Structural Mean Models and estimate their parameters using G-estimation to obtain unbiased estimates of the effect of continued breastfeeding on infant growth (weight or length) at one year of age. We also implement a modified version of the G-estimation algorithm that is asymptotically unbiased; this is the first real-data application of the algorithm. Finally, we compare the decision rules implied by the G-estimates with the decision implied by a myopic optimization estimation approach, that is, we compare with decision rules that maximize response in the short-term. The breastfeeding regimes selected by each of the three models are optimal in the sense that specific criteria were optimized; the criteria considered here (maximizing weight or length) were chosen for simplicity, but may not lead to better overall health. We demonstrate in the context of a breastfeeding promotion intervention trial that optimal myopic decision strategies do not coincide with strategies that optimize a longer-term response. Please see the online supplements for a correction to this article.
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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 | 0.038 | 0.150 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".