The development of CHAMP: a checklist for the appraisal of moderators and predictors
Bibliographic record
Abstract
BACKGROUND: Personalized healthcare relies on the identification of factors explaining why individuals respond differently to the same intervention. Analyses identifying such factors, so called predictors and moderators, have their own set of assumptions and limitations which, when violated, can result in misleading claims, and incorrect actions. The aim of this study was to develop a checklist for critically appraising the results of predictor and moderator analyses by combining recommendations from published guidelines and experts in the field. METHODS: Candidate criteria for the checklist were retrieved through systematic searches of the literature. These criteria were evaluated for appropriateness using a Delphi procedure. Two Delphi rounds yielded a pilot checklist, which was tested on a set of papers included in a systematic review on reinforced home-based palliative care. The results of the pilot informed a third Delphi round, which served to finalize the checklist. RESULTS: Forty-nine appraisal criteria were identified in the literature. Feedback was obtained from fourteen experts from (bio)statistics, epidemiology and other associated fields elicited via three Delphi rounds. Additional feedback from other researchers was collected in a pilot test. The final version of our checklist included seventeen criteria, covering the design (e.g. a priori plausibility), analysis (e.g. use of interaction tests) and results (e.g. complete reporting) of moderator and predictor analysis, together with the transferability of the results (e.g. clinical importance). There are criteria both for individual papers and for bodies of evidence. CONCLUSIONS: The proposed checklist can be used for critical appraisal of reported moderator and predictor effects, as assessed in randomized or non-randomized studies using individual participant or aggregate data. This checklist is accompanied by a user's guide to facilitate implementation. Its future use across a wide variety of research domains and study types will provide insights about its usability and feasibility.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.701 | 0.886 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads 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".