Instrumental variable analysis: choice of control variables is critical and can lead to biased results
Bibliographic record
Abstract
Abstract Instrumental variable (IV) analysis is a widely used technique in econometrics to estimate causal effects in the presence of confounding. A recent application of this technique was used in a high-profile analysis in JAMA Internal Medicine to estimate the effect of cefepime, a broad-spectrum antibiotic, on mortality in severe infection. There has been ongoing concern that piperacillin-tazobactam, another broad-spectrum antibiotic with greater anaerobic activity might be inferior to cefepime, however this has not been shown in randomized controlled trials. The authors used an international shortage of piperacillin-tazobactam as an instrument, as during this shortage period, cefepime was used as an alternative. The authors report a strong mortality effect (5% absolute increase) with piperacillin-tazobactam. In this paper, we closely examine this estimate and find it is likely conditional on inclusion of a control variable (metronidazole usage). Inclusion of this variable is highly likely to lead to collider bias, which we show via simulation. We then generate estimates unadjusted for metronidazole which are much closer to the null and may represent residual confounding or confounding by indication. We highlight the ongoing challenge of collider bias in empirical IV analyses and the potential for large biases to occur. We finally suggest the authors consider including these unadjusted estimates in their manuscript, as the large increase in mortality reported with piperacillin-tazobactam is unlikely to be true.
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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 | 0.230 | 0.577 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 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; 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".