Adjusting to Imbalance: When to Statistically Adjust for Differences Between Treatment Groups in Clinical Studies
Bibliographic record
Abstract
Background/Objective: Surgeons conducting randomized trials are sometimes left with the dilemma of unbalanced characteristics between the treatment and control groups. It remains debatable how baseline differences between treatment and control groups should be handled in surgical studies. Some investigators advocate ignoring the imbalanced variable whiles others believe an “adjusted analysis” should be performed, correcting for the variable imbalanced between groups. We reviewed the rationale and conduct of adjusted and unadjusted analyses in surgical clinical trials. Methods: We conducted computerized and hand searches to identify published surgical randomized controlled trials in the British Medical Journal (BMJ), Journal of the American Medical Association (JAMA), New England Journal of Medicine, The Lancet, Journal of Bone and Joint Surgery (JBJS - American Volume), and Journal of Bone and Joint Surgery (JBJS - British Volume) between January 2000 and April 2003. Three reviewers abstracted information about imbalances in baseline variables and variable adjustment. Discrepancies were resolved by consensus. Results: We identified 72 randomized trials. Studies presented an average of 10.3 ± 7.7 baseline variables. Fifteen trials (20.8 %) reported imbalances in baseline characteristics of their study populations. Twenty-three trials (31.9 %) reported both unadjusted and covariate-adjusted results but unadjusted analyses received more emphasis in 18 trials (78.3 %). The studies' conclusions were changed in 3 trials (13 %) when an adjusted analysis was conducted. Conclusions: Our review has identified important problems with the reporting and rationale for adjusting for imbalances in patient groups in surgical clinical trials. Investigators conducting clinical comparative studies should endeavor to report the rationale for conducting adjusted analyses of their data. In the absence of such information, readers should rely more on the simple unadjusted results of a study.
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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 | 0.798 | 0.931 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.016 | 0.020 |
| Bibliometrics | 0.026 | 0.025 |
| Science and technology studies | 0.005 | 0.015 |
| Scholarly communication | 0.015 | 0.025 |
| Open science | 0.014 | 0.008 |
| Research integrity | 0.014 | 0.014 |
| Insufficient payload (model declined to judge) | 0.004 | 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".