Establishing Clinically Meaningful Change on Outcome Assessments Frequently Used in Trials of Mild Cognitive Impairment Due to Alzheimer's Disease
Bibliographic record
Abstract
BACKGROUND: Consensus is lacking on what constitutes a meaningful score change for individual patients on clinical outcome assessments (COAs) that are commonly used in clinical trials of Alzheimer's disease. Such thresholds are one important approach to help contextualize trial results and demonstrate meaningful treatment benefit. OBJECTIVES: To estimate meaningful within-patient change thresholds for the Clinical Dementia Rating Scale - Sum of Boxes (CDR-SB), Alzheimer's Disease Assessment Scale - Cognitive Subscale (ADAS-Cog), and the Mini-Mental State Examination (MMSE) among participants with mild cognitive impairment (MCI). DESIGN: Retrospective anchor- and distribution-based analyses of data from the ADC-008 (NCT00000173) study were used to estimate thresholds for meaningful within-patient change on the target measures. SETTING: Analyses were conducted using data from ADC-008 a Phase III, multicenter, randomized, double-blind, placebo-controlled, parallel-group study among participants with the amnestic subtype of MCI, which was conducted by the Alzheimer's Disease Cooperative Study (ADCS) between March 1999 and January 2004 in the United States and Canada. PARTICIPANTS: Analyses were based on 769 eligible participants who completed the baseline assessment from 69 ADCS sites in the United States and Canada. MEASUREMENTS: The target outcome measures for this analysis included the CDR-SB, the ADAS-Cog, and the MMSE. The anchor measures for this analysis included the Global Deterioration Scale and the MCI-Clinical Global Impression of Change. RESULTS: Focusing on the 12-month time point, within-patient increases of 1-2.5 points in the CDR-SB and increases of 2-5 points on the 11-item ADAS-Cog and 13-item ADAS-Cog, on average, reflect minimal-to-moderate levels of deterioration, respectively. CONCLUSIONS: These thresholds may be useful to aid the interpretation of Alzheimer's disease clinical trial data by illustrating meaningful within-patient progression over the course of a clinical trial via supplementary progressor analyses, which may in turn be informative for treatment decisions. Estimates generated via these methods are specifically intended to evaluate within-patient change and are not intended to assess the magnitude and meaningfulness of differences between group-level changes over time.
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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 | 0.369 | 0.467 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".