Marginal structural models in clinical research: when and how to use them?
Bibliographic record
Abstract
Marginal structural models are a multi-step estimation procedure designed to control for the effect of confounding variables that change over time, and are affected by previous treatment. When a time-varying confounder is affected by prior treatment standard methods for confounding control are inappropriate, because over time the covariate plays both the role of confounder and mediator of the effect of treatment on outcome. Marginal structural models first calculate a weight to assign to each observation. These weights reflect the extent to which observations with certain characteristics (covariate values) are under-represented or over-represented in the sample with the respect to a target population in which these characteristics are balanced across treatment groups. Then, marginal structural models estimate the outcome of interest taking into account these weights. Marginal structural models are a powerful method for confounding control in longitudinal study designs that collect time-varying information on exposure, outcome and other covariates.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.154 | 0.409 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.001 | 0.009 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.008 | 0.007 |
| Research integrity | 0.007 | 0.014 |
| Insufficient payload (model declined to judge) | 0.011 | 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".