Estimation of dose–response functions for longitudinal data using the generalised propensity score
Bibliographic record
Abstract
In a longitudinal study of dose-response, it is often necessary to adjust for confounding or non-compliance, which may otherwise compromise the estimation of the true effect of a treatment. Using an approach based on the generalised propensity score (GPS)--a generalisation of the classical, binary treatment propensity score--it is possible to construct a balancing score that provides an estimation procedure for the true (unconfounded) direct effect of dose on response. Previously, the GPS has been applied only in a single interval setting; in this article, we extend the GPS methodology to the longitudinal setting to estimate the direct effect of a continuous dose on a longitudinal response. The methodology is applied to two simulated examples, and a real longitudinal dose-response investigation, the Monitored Occlusion Treatment of Amblyopia Study (MOTAS). In the treatment of childhood amblyopia, a common ophthalmological condition, occlusion therapy (patching) was for many decades the standard medical treatment, despite the fact that its efficacy was not quantified. MOTAS was revolutionary, as it was the first study to obtain precise measurements of the amount of occlusion each study participant received over the course of the study.
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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 | 0.107 | 0.186 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".