Multidomain trials to prevent dementia: addressing methodological challenges
Bibliographic record
Abstract
BACKGROUND: Multidomain trials to prevent dementia by simultaneously targeting multiple risk factors with non-pharmacological lifestyle interventions show promise. Designing trials to evaluate the efficacy of individual interventions and their combinations is methodologically challenging. Determining the efficacy is, nevertheless, important to individuals, payers, and for resource allocations to support intervention implementation. MAIN BODY: The central rationale for seminal trials improving cardiovascular health or reducing falls risk in older adults is that multifactorial conditions may be amenable to improvement by simultaneously targeting multiple modifiable risk factors. Similar reasoning underlies lifestyle interventions to reduce dementia risk using combinations of physical exercise, cognitive training, diet, amelioration of vascular-metabolic risk factors, and improving sleep quality. Randomizing individuals with at least two modifiable risk factors to "standardly tailored" interventions to mitigate their risk factors, versus a comparator arm, will yield an unbiased estimate of the cumulative average effect of modifying more versus fewer risk factors. The between-group difference in the cognitive primary outcome will reflect both the main effects of the mitigated risk factors, as well as their synergistic effects. However, given the positive trial results, there are inherent challenges in quantifying post hoc which components, or combination of components, were responsible for improvements in cognition. Here, we elaborate on these methodological challenges and important considerations in using a standardly tailored design with two arms (one consisting of multidomain interventions tailored to participants' risk profiles and another consisting of active control conditions). We compare this approach to fully factorial designs and highlight the disadvantages and advantages of each. We discuss partial solutions, including analytical strategies such as risk reduction scores that measure reductions in the number or severity of risk factors in each study arm. Positive results can support the causal inference that between-group differences in the primary cognitive outcome were due to risk factor modification. CONCLUSION: Standardly tailored designs are pragmatic and feasible evaluations of multidomain interventions to reduce dementia risk. We propose sensitivity and exploratory analyses of between-group reductions in the severity of risk factors, as a methodology to bolster causal inferences that between-group differences in the primary cognitive outcome are due to the risk factors modified.
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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 | 0.722 | 0.832 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.011 | 0.010 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.008 | 0.009 |
| Research integrity | 0.008 | 0.013 |
| 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; 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".