The CREATE Method for Expressing Continuous Outcome Data in Absolute Terms for Use in Patient Treatment Decision Aids
Bibliographic record
Abstract
BACKGROUND: Patient decision aids (PtDAs) supplement advice from health care professionals by communicating the absolute risk or benefit of treatment options (i.e., X/100). As such, PtDAs have been amenable to binary outcomes only. We aimed to develop and test the validity of the Conversion to Risk Estimates through Application of Normal Theory (CREATE) method for estimating absolute risk based on continuous outcome data. METHODS: CREATE is designed to derive an estimate of the proportion of those who experience a clinically relevant degree of change (CRDoC). We used a 2-stage validation process using real and simulated change score data, respectively. First, using raw data from published intervention trials, we calculated the proportion of patients with a CRDoC and compared that with our CREATE-derived estimate using chi-square tests of association. Second, 200,000 simulated distributions of change scores were generated with widely varying distribution characteristics. Actual and CREATE-derived estimates were compared for each simulated distribution, and relative differences were summarized graphically. RESULTS: The absolute difference between the estimated and actual CRDoC did not exceed 5% for any of the samples based on real data. Applying the CREATE method to 200,000 simulated scenarios demonstrated that the CREATE method should be avoided for outcomes where the underlying distribution can be reasonably assumed to have high levels of skew or kurtosis. CONCLUSION: Our results suggest that standard statistical theory can be used to estimate continuous outcomes in absolute terms with reasonable accuracy for use in PtDAs; caution is advised if outcome summary statistics are assumed to have been derived from highly skewed distributions.
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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 | 0.065 | 0.346 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.043 | 0.009 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".