Standardizing to target populations in multisite studies using inverse odds and augmented inverse probability weighting
Bibliographic record
Abstract
Distributed network studies assess treatment effects across heterogeneous populations by pooling data from multiple sites.This work shows how baseline covariate data from the entire cohort, together with treatment and outcome data from source population sites, can identify treatment effect in prespecified target sites.We propose an inverse odds weighted (IOW) augmented inverse probability weighting (AIPW) framework, improving robustness in transportability settings.Unlike standard AIPW, IOW models source population membership from observational data, introducing a third model beyond the traditional doubly robust estimator.We first evaluate the performance of the proposed estimator in a four-site simulation study, where each site was standardized to target on either the three smallest sites combined or the single smallest site.The estimated risk-difference confirmed that the IOW-AIPW framework preserves double-robustness.We then apply the method to a substantive example using data from Clinical Practice Research Datalink (CPRD), creating an artificial distributed network where each site represents one distinct geographic region.We target on the smallest region and compared the initiation of metformin and sulfonylurea with respect of mortality.Both the simulation study and substantive example showed that IOW-based standardization enhances precision and interpretability in multi-site studies and helps explain differences in estimates across study segments.viii I would also like to express my heartfelt thanks to all my friends and classmates, for the insightful discussions we had in the lounge, for the fun hangout time.I am truly lucky to have all of you make my two years unforgettable.I would also like to thank my lifelong friends whose emotional support all these years is invaluable to me, in particular, Yijia, Yuhong, and Zhifei.Finally, I would like to thank my family, especially my parents and my wife Ruohan, for being a constant source of love, encouragement, and unconditional support.
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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 | 0.066 | 0.193 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".