Comparing <scp>IPCW</scp> Models to Adjust for Informative Censoring During <scp>COVID</scp> ‐19 Using Data From the Clinical Practice Research Datalink
Bibliographic record
Abstract
PURPOSE: Observational comparative studies can be analyzed using intention-to-treat (ITT) (i.e., initial-treatment) or as-treated (AT) (i.e., per-protocol) approaches to estimate distinct treatment effects. Unfortunately, AT analyses have an increased vulnerability to selection bias from informative censoring. While methods for informative censoring adjustment are well established, the nuances of their implementation are less well documented. METHODS: We compared marginal hazard ratios for all-cause mortality from ITT and AT analyses comparing new users of selective serotonin reuptake inhibitors (SSRIs) and serotonin-norepinephrine reuptake inhibitors (SNRIs) in the clinical practice research datalink from 2019 to 2022 using inverse probability of treatment weights. We created inverse probability of censoring weights (IPCW) using (A) non-lagged and (B) lagged models to adjust for informative censoring in the AT analyses. We replicated analyses comparing acetylcholinesterase inhibitor and angiotensin receptor blocker initiators to assess the impact of IPCW in a different context. RESULTS: We identified 335 469 SSRI initiators and 24 318 SNRI initiators. While AT estimates (HR: 1.50, 95% CI: 1.30-1.74) were further from the null than ITT estimates (HR: 1.22, 95% CI: 1.12-1.32), applying IPCW attenuated AT estimates using both lagged and non-lagged models (lagged HR: 1.24, 95% CI: 1.08-1.44; non-lagged HR: 1.16, 95% CI: 1.00-1.33). In the 337 981 antihypertensive initiators, however, IPCW did not influence AT estimates. CONCLUSIONS: Younger patients were more likely to discontinue SSRIs than SNRIs, resulting in biased AT estimates closer to estimates in older patients. IPCW attenuated this bias, highlighting the utility of weighting when censoring is linked to patient characteristics.
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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 | 0.218 | 0.484 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.014 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.042 | 0.004 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".