Does Piperacillin-Tazobactam Increase Mortality Risk Compared With Cefepime? Collider Bias and the Importance of Assumptions in Instrumental Variable Analyses
Bibliographic record
Abstract
BACKGROUND: Instrumental variable (IV) analysis is a statistical method allowing causal inference under certain assumptions. A recent high-profile IV analysis suggested cefepime was superior to piperacillin-tazobactam in treating sepsis. This study used a worldwide piperacillin-tazobactam shortage as an IV to infer mortality effects. However, this result starkly contrasts with the well-powered ACORN trial, which showed no effect. We discuss important limitations of the IV study, potentially explaining this discrepancy. METHODS: We used causal diagrams and the potential outcomes framework to describe potential biases. We identified 2 sources: (1) statistical adjustment for metronidazole treatment, leading to collider bias, and (2) operationalization of the treatment variable (exposure coarsening). We performed simulations demonstrating collider bias can explain the results. Finally, we used summary data from the original paper to obtain alternative causal estimates robust to these biases. RESULTS: Adjusting for metronidazole, a choice influenced by both the IV (via initial antibiotic) and underlying factors such as disease severity, induces collider bias. Analyses not adjusting for metronidazole show no strong evidence for a mortality difference. However, bias risk from exposure coarsening remains even without adjustment. Re-analyzing summary data provides no compelling evidence for a benefit of cefepime over piperacillin-tazobactam. CONCLUSIONS: The recent IV analysis does not support a mortality benefit for cefepime; results appear dependent on incorrect analytical choices introducing bias. Clinicians should be aware of IV analysis complexities and assumptions when making causal inferences from observational data, especially when results contradict high-quality trials and antibiotic choice is the exposure.
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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 | 0.268 | 0.528 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.008 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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; 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".