Combining Direct and Proxy Assessments to Reduce Attrition Bias in a Longitudinal Study
Bibliographic record
Abstract
Retaining severely impaired individuals poses a major challenge in longitudinal studies of determinants of dementia or memory decline. In the Health and Retirement Study (HRS), participants complete direct memory assessments biennially until they are too impaired to complete the interview. Thereafter, proxy informants, typically spouses, assess the subject's memory and cognitive function using standardized instruments. Because there is no common scale for direct memory assessments and proxy assessments, proxy reports are often excluded from longitudinal analyses. The Aging, Demographics, and Memory Study (ADAMS) implemented full neuropsychological examinations on a subsample (n=856) of HRS participants, including respondents with direct or proxy cognitive assessments in the prior HRS core interview. Using data from the ADAMS, we developed an approach to estimating a dementia probability and a composite memory score on the basis of either proxy or direct assessments in HRS core interviews. The prediction model achieved a c-statistic of 94.3% for DSM diagnosed dementia in the ADAMS sample. We applied these scoring rules to HRS core sample respondents born 1923 or earlier (n=5483) for biennial assessments from 1995 to 2008. Compared with estimates excluding proxy respondents in the full cohort, incorporating information from proxy respondents increased estimated prevalence of dementia by 12 percentage points in 2008 (average age=89) and suggested accelerated rates of memory decline over time.
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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 | 0.356 | 0.434 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".