The PROMIS of Better Outcome Assessment: Responsiveness, Floor and Ceiling Effects, and Internet Administration
Bibliographic record
Abstract
OBJECTIVE: Use of item response theory (IRT) and, subsequently, computerized adaptive testing (CAT), under the umbrella of the NIH-PROMIS initiative (National Institutes of Health-Patient-Reported Outcomes Measurement Information System), to bring strong new assets to the development of more sensitive, more widely applicable, and more efficiently administered patient-reported outcome (PRO) instruments. We present data on current progress in 3 crucial areas: floor and ceiling effects, responsiveness to change, and interactive computer-based administration over the Internet. METHODS: We examined nearly 1000 patients with rheumatoid arthritis and related diseases in a series of studies including a one-year longitudinal examination of detection of change; compared responsiveness of the Legacy SF-36 and HAQ-DI instruments with IRT-based instruments; performed a randomized head-to-head trial of 4 modes of item administration; and simulated the effect of lack of floor and ceiling items upon statistical power and sample sizes. RESULTS: IRT-based PROMIS instruments are more sensitive to change, resulting in the potential to reduce sample size requirements substantially by up to a factor of 4. The modes of administration tested did not differ from each other in any instance by more than one-tenth of a standard deviation. Floor and ceiling effects greatly reduce the number of available subjects, particularly at the ceiling. CONCLUSION: Failure to adequately address floor and ceiling effects, which determine the range of an instrument, can result in suboptimal assessment of many patients. Improved items, improved instruments, and computer-based administration improve PRO assessment and represent a fundamental advance in clinical outcomes research.
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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 | 0.061 | 0.165 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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; 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".