Predicting the Time to Clinically Worsening in Mild Cognitive Impairment Patients and its Utility in Clinical Trial Design by Modeling a Longitudinal Clinical Dementia Rating Sum of Boxes from the ADNI Database
Bibliographic record
Abstract
BACKGROUND: Growing interest in treating Alzheimer's disease (AD) patients in the earliest stages requires new clinical endpoints. Currently, there is no established clinical endpoint or treatment duration for mild cognitive impairment (MCI) trials. OBJECTIVE: This analysis attempts to answer "how long the MCI clinical trial would be necessary" using the Clinical Dementia Rating Sum of Boxes (CDR-SB) as a clinical endpoint, where CDR-SB is an example of a suitable tool to assess both cognition and function as a single primary efficacy outcome. METHODS: A longitudinal model was developed to predict the CDR-SB time-profile. The CDR-SB is considered ideal to assess both cognition and function as a single primary endpoint in MCI trials. The median time for clinically "worsening", defined using several thresholds for change from baseline, was calculated using individual CDR-SB predictions. Covariates predictive of worsening were also evaluated. RESULTS: The median time to a 1-point change in CDR-SB was approximately 2 years in MCI patients. Higher baseline severity in disease, lower hippocampal volume, and ApoE4 carrier status were significant covariates predicting shorter times to worsening (faster progress). The results indicate that at least a 2-year trial would be necessary with 30% (or more) disease modifying drug with a sample size of n = 350 to detect the significant difference from placebo (80% power) and to achieve the target mean effect size of 0.5 point change in CDR-SB. CONCLUSION: Predictions of CDR-SB changes from a longitudinal model are able to inform study design and possible enrichment strategies, based on covariate analyses, for prospective planning of clinical trials in MCI patients.
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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 | 0.123 | 0.110 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".