Relation of Distribution- and Anchor-Based Approaches in Interpretation of Changes in Health-Related Quality of Life
Bibliographic record
Abstract
BACKGROUND: Approaches to interpretation of quality of life changes in clinical trials have fallen into two camps: those that rely on the distribution of changes and the Effect Size (ES), and those that use some external anchor, such as patient judgments of change, which is then used to compute a Minimally Important Difference (MID), the proportion benefiting from treatment, p(B), and the Number Needed to Treat (NNT). OBJECTIVE: To examine the relationship between the ES and p(B), and the impact of the MID on this relationship. METHODS: Simulation was used based on a normal distribution to compute the proportion of patients benefiting in both parallel group and crossover designs, for various values of the ES and the MID. The agreement of the simulation with empirical data from four studies of asthma and respiratory disease was assessed. The effect of skewness in the distributions of change scores on the relationship between ES and p(B) was also examined. RESULTS: The simulation showed a near-linear relationship between ES and p(B), which was nearly independent of the value of the MID. Agreement of the simulation with the empirical data were excellent. Although the curves differed for crossover and parallel group designs, the general form was similar. Introducing moderate skew into the distributions had minimal impact on the relationship. CONCLUSIONS: The proportion of patients who will benefit from treatment can be directly estimated from the ES, and is nearly independent of the choice of MID. Effect size and anchor based approaches provide equivalent information in this situation.
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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 | 0.386 | 0.732 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".