Comparing two counterfactual-outcome approaches in causal mediation analysis of a multicategorical exposure: An application for the estimation of the effect of maternal intake of inhaled corticosteroids doses on birthweight
Bibliographic record
Abstract
Although medical research frequently involves an exposure variable with three or more discrete levels, detailed presentations of mediation techniques for dealing with multicategorical (multilevel) exposures are sparse. In this paper, we study two causal mediation approaches applicable to such a type of exposure for continuous mediator and outcome: the closed-form regression-based approach of Valeri and VanderWeele, and the marginal structural model-based approach of Lange, Vansteelandt, and Bekaert. While the consideration of multicategorical exposures is found explicitly addressed in the literature for the latter approach, this is, to our knowledge, not yet the case for the former. We first illustrate the application of the two aforementioned approaches to assess the dose-response relationship between maternal intake of inhaled corticosteroids and birthweight, where this relationship is potentially mediated by gestational age. More specifically, we provide a precise roadmap for the application of the regression-based approach and of the marginal structural model-based approach on our cohort of pregnancies. Expressions for the natural direct and indirect effects associated with our categorical exposure are provided and, for the regression-based approach, analytic formulas for standard error calculation using the delta method are presented for these effects. Second, a simulation study which mimics our data is presented to add to current knowledge on these causal mediation techniques. Results from this study highlight the relevance to assess robustness of mediation results obtained from multicategorical exposures, most notably for the least prevalent of exposure categories.
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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 | 0.164 | 0.295 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".