P2‐577: THE VARIABILITY IN INPUT PARAMETER VALUES IN MODELS ESTIMATING THE EFFECTIVENESS OF HYPOTHETICAL DISEASE MODIFYING TREATMENTS FOR ALZHEIMER'S DISEASE
Bibliographic record
Abstract
Results from cost-effectiveness (CE) models are sensitive to input parameter values. We compared selected input parameter values in models that estimate the effectiveness of hypothetical disease-modifying treatments (DMTs) for Alzheimer's disease (AD). A targeted literature search of the Medline database from 2010 to present identified disease models for AD that included the impact of hypothetical DMTs. Input parameter values were compared for rate of conversion from mild cognitive impairment (MCI) to dementia, institutionalization and excess mortality rates, utility weights, and costs by disease severity. Eight published models were reviewed: three patient-level simulations, four Markov models, and one using observed MMSE decline rates. Seven models included predementia health states. Annual rates of conversion from MCI to mild AD in the Markov models were similar (∼27%) for populations with biomarker evidence of AD. Conversion rates were lower (∼10%) if all patients with MCI were included in the population. The percentage of patients institutionalized for those with severe AD was 39.3% in two models compared with 76.22% in two other models. A fifth model used annual institutionalization rates of 1.2% for mild dementia, 3.4% for moderate dementia, and 6.6% for severe dementia. Two models assumed no variation in mortality by disease stage. Two other models used an additive factor for mortality for moderate (0.055 or 0.11) and severe (0.11) dementia. In two other models, multiplicative mortality risks were assumed either only for moderate (× 2.52) and severe (× 7.3) dementia or for MCI (× 1.48) and all dementia severities (× 2.84). Finally, differences in utility and costs between MCI and mild AD varied among the models. For example, the decrease in utility between MCI and mild AD was 0.05 in one model and 0.20 in another study. In addition, two models assumed no excess costs for those with MCI, while excess costs derived from observational data were included in two different models. DMTs under development are targeted for those with MCI or mild AD. This review highlights the considerable variability among disease models in transition, institutionalization, excess mortality rates and utility and cost inputs across these two health states.
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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 | 0.023 | 0.112 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".