NONRANDOMIZED STUDIES: THE HAZARDOUS PRACTICE OF TESTING FOR BASELINE IMBALANCES
Bibliographic record
Abstract
Nonrandomized studies are increasingly used to evaluate interventions where randomization is not feasible or desired such as with policy reforms or practice change. A common practice is to statistically compare baseline characteristics between the control and intervention group to determine imbalances and confounders for model adjustment. This practice, however, has been shown to be inappropriate since false positives and negatives are not controlled. Moreover, the use of this practice to select confounders for model adjustment can introduce rather than protect against bias. The goals of this assessment were 1) to assess current publishing guidelines regarding baseline testing and 2) to elaborate recommendations. The guidelines from 16 high-impact journals were assessed. The journals did not provide direct guidance and referred authors to one or more of the following guidelines: ICMJE (International Committee of Medical Journal Editors), STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) and the Equator Network, including TREND (Transparent Reporting of Evaluations with Nonrandomized Designs). ICMJE provided no specific guidance and referred to STROBE. While STROBE did correctly recommend choosing confounders at study design stage; no guidance on specific analytical methods were given. Finally, TREND actually promoted baseline testing. Experts recommend that adjustment variables should be chosen at the design stage based on clinical knowledge. Sensitivity analyses, such as the use of doubly-robust methods, are also recommended. In conclusion, reporting guidelines need to be updated to offer more appropriate methods. Journal editors have the power to promote good research by explicitly discouraging baseline testing in nonrandomized studies.
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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 | 0.794 | 0.890 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.009 | 0.006 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.004 | 0.026 |
| Scholarly communication | 0.015 | 0.017 |
| Open science | 0.009 | 0.008 |
| Research integrity | 0.010 | 0.015 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".