Use of Self-Matching to Control for Stable Patient Characteristics While Addressing Time-Varying Confounding on Treatment Effect: A Case Study of Older Intensive Care Patients
Bibliographic record
Abstract
Exposure-crossover design offers a non-experimental option to control for stable baseline confounding through self-matching while examining causal effect of an exposure on an acute outcome. This study extends this approach to longitudinal data with repeated measures of exposure and outcome using data from a cohort of 340 older medical patients in an intensive care unit (ICU). The analytic sample included 92 patients who received ≥1 dose of haloperidol, an antipsychotic medication often used for patients with delirium. Exposure-crossover design was implemented by sampling the 3-day time segments prior (Induction) and posterior (Subsequent) to each treatment episode of receiving haloperidol. In the full cohort, there was a trend of increasing delirium severity scores (Mean±SD: 4.4±1.7) over the course of the ICU stay. After exposure-crossover sampling, the delirium severity score decreased from the Induction (4.9) to the Subsequent (4.1) intervals, with the treatment episode falling in-between (4.5). Based on a GEE Poisson model accounting for self-matching and within-subject correlation, the unadjusted mean delirium severity scores was -0.55 (95% CI: -1.10, -0.01) points lower for the Subsequent than the Induction intervals. The association diminished by 32% (-0.38, 95%CI: -0.99, 0.24) after adjusting only for ICU confounding, while being slightly increased by 7% (-0.60, 95%CI: -1.15, -0.04) when adjusting only for baseline characteristics. These results suggest that longitudinal exposure-crossover design is feasible and capable of partially removing stable baseline confounding through self-matching. Loss of power due to eliminating treatment-irrelevant person-time and uncertainty around allocating person-time to comparison intervals remain methodological challenges.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.068 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".