168 Comparative safety and efficacy of cognitive enhancers for Alzheimer’s dementia: an individual patient data network meta-analysis
Bibliographic record
Abstract
Objectives Alzheimer’s dementia (AD) is the most common type of dementia. However, it is unclear which cognitive enhancer is optimal for severe AD. Patient-level data from people with AD can be helpful to explore patient-level variation per treatment response. Pooling individual patient data (IPD) from multiple randomised clinical trials (RCTs) of clinical interventions is considered the ‘gold standard’ analysis. To examine the comparative efficacy and safety of cognitive enhancers by patient characteristics, such as AD severity and sex, and to assess treatment-by-covariate interactions through IPD network meta-analysis (NMA). Method We searched for RCTs with adults comparing cognitive enhancers, and addressing cognition using the Mini-Mental State Examination (MMSE) and/or serious adverse events (SAEs). For eligible RCTs, we requested IPD from authors, sponsors and data sharing platforms. We assessed for consistency between results from published RCTs and provided IPD. We applied an available case analysis for each study, but we plan to explore the impact of missing data through the use of informative missingness parameters in NMA. We captured reasons for missing participants and time to SAE. We conducted a 2-stage analysis: at 1st stage IPD from included studies were aggregated to study-level summary; at 2nd stage the trial parameter estimates were synthesized in a random-effects NMA. We summarized evidence using the odds ratio (OR) and mean difference (MD), respectively. We combined aggregated data from RCTs for which we were unable to obtain IPD. Results We included 108 RCTs and received IPD for 17 (16%) RCTs. Of the 17 RCTs, we were able to include 12 RCTs in our NMA with complete data. Access to IPD via proprietary sponsor-specific platforms restricted us from combining IPD in a one-stage NMA model. In most IPD, we encountered a high dropout rate (up to 72%), for which most publications used the last observation carried forward imputation method. NMA results including IPD and/or aggregate data will be presented at the EBMLive. Conclusions An advantage of our IPD-NMA is that we were able to include outcome data, which were not reported in the original publications. Our study will provide insight on personalized medicine for patients with AD.
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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 | 0.044 | 0.074 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.056 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".