Continuous‐Time Causal Inference With Marked Point Process Weights: An Example on Sodium‐Glucose Co‐Transporters 2 Inhibitor Medications and Urinary Tract Infection
Bibliographic record
Abstract
Treatment-confounder feedback is present in time-to-recurrent or longitudinal event analysis when time-dependent confounders are themselves influenced by previous treatments. Conventional models produce misleading statistical inference of causal effects due to conditioning on these factors on the causal pathway. Marginal structural models are often applied to quantify the causal treatment effect, estimated using longitudinal weights that mimic the randomization procedure by balancing the covariate distributions across the treatment groups. The weights are usually constructed in discrete time intervals, which is appropriate if the follow-up visits are scheduled and regular. However, in primary care, visit times can be irregular and informative, and the treatment history consists of duration and doses. This can be modeled through a continuous-time marked point process. We constructed a continuous-time marginal structural model to estimate the effect of cumulative exposure to Sodium-Glucose co-Transporters 2 Inhibitor (SGLT-2i) medications on time-to-recurrent urinary tract infection (UTI). We used a cohort of type II diabetes patients with chronic kidney disease and constructed a marked point process that characterized the recurrent flare episodes of primary care visits (i.e., point process) with marks for the multinominal dose (none, low, high) of SGLT-2i medications and recurrent episodes of UTI. We applied the stabilized and optimal treatment weights to estimate the hypothesized causal effect. Our results are concordant with earlier findings in which the recurrent episodes of UTI did not increase when patients were prescribed low dose or high dose of SGLT-2i medications.
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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 | 0.047 | 0.140 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".