Clarifying the causal contrast: An empirical example applying the prevalent new user study design
Bibliographic record
Abstract
Abstract Purpose The prevalent new user design extends the active comparator new user design to include patients switching to a treatment of interest from a comparator. We examined the impact of adding “switchers” to incident new users on the estimated hazard ratio (HR) of hospitalized heart failure. Methods Using MarketScan claims data (2000–2014), we estimated HRs of hospitalized heart failure between patients initiating GLP‐1 receptor agonists (GLP‐1 RA) and sulfonylureas (SU). We considered three estimands: (1) the effect of incident new use; (2) the effect of switching; and (3) the effect of incident new use or switching, combining the two population. We used time‐conditional propensity scores (TCPS) and time‐stratified standardized morbidity ratio (SMR) weighting to adjust for confounding. Results We identified 76 179 GLP‐1 RA new users, of which 12% were direct switchers (within 30 days) from SU. Among incident new users, GLP‐1 RA was protective against heart failure (adjHRSMR = 0.74 [0.69, 0.80]). Among switchers, GLP‐1 RA was not protective (adjHRSMR = 0.99 [0.83, 1.18]). Results in the combined population were largely driven by the incident new users, with GLP‐1 RA having a protective effect (adjHRSMR = 0.77 [0.72, 0.83]). Results using TCPS were consistent with those estimated using SMR weighting. Conclusions When analyses were conducted only among incident new users, GLP‐1 RA had a protective effect. However, among switchers from SU to GLP‐1 RA, the effect estimates substantially shifted toward the null. Combining patients with varying treatment histories can result in poor confounding control and camouflage important heterogeneity.
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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 | 0.400 | 0.591 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".