Dropouts and refusals in observational studies
Bibliographic record
Abstract
The success of prevention trials of Alzheimer disease and other dementias (AD/dementia) hinges on their ability to recruit and retain at-risk study populations. Losing subjects is a threat to the power to detect a treatment effect and, potentially, to the validity of these studies. Observational cohort studies accumulate data around participant outcomes that can help to inform the design of future prevention trials. Our objectives were to investigate the rates of refusal and dropout within observational cohort studies and to evaluate their characteristics and impact. This study examined data from the Canadian Cohort Study of Cognitive Impairment and Related Dementias (ACCORD), a 2-year observational cohort study of patients newly referred to dementia clinics. The sample included 124 Not Cognitively Impaired (NCI) and 342 Cognitively Impaired Not Demented (CIND) subjects. Subjects who refused initial neuropsychological (NP) testing were compared to those who completed NP testing and subjects who dropped out to those who attended follow-up. Refusal was common, with 40% of subjects not completing neuropsychological testing at baseline. Dropout was also substantial, with 55% lost to the 2-year follow-up. Subjects who refused NP testing were significantly older and less educated. CIND refusers had lower cognitive and functional scores at entry and a 2-year progression rate to dementia twice as high as that of non-refusers. CIND dropouts also had lower baseline cognitive and functional scores. These observations suggest that dropouts and refusals in prevention trials include those subjects who are at high risk for progression to AD/dementia. Targeted strategies to retain these individuals within prevention studies will be needed to achieve sufficient study power and validity.
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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 | 0.310 | 0.534 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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; 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".