Statistical data harmonization for neuropsychological test battery conversion
Bibliographic record
Abstract
Abstract Background A current problem scientists and clinicians face when studying cognitive impairment is how to harmonize scores across neuropsychological tests evaluating the same cognitive domains in a longitudinal analysis. Prior point conversion methods typically do not allow quantification of uncertainty and may be based on strong distributional assumptions. Our framework was motivated by an interest in assessing the uncertainty when converting scores. Method We established new statistical methodology for converting C1 and C2 neuropsychological batteries of the National Alzheimer’s Coordinating Center Uniform Data Set collected by the NIA Alzheimer’s Disease Research Centers Program. We adapted methodology from the measurement error and latent variable statistical modeling techniques and proposed an algorithm to give point and interval conversions. We developed parametric and non‐parametric models to achieve this task, allowing one to control for important traits such as age, education level and sex at birth. Additionally, we developed variational inference and functional Expectation Maximization algorithms to fit these models and implemented them in R. This method was compared to existing methodology using Z‐score conversions or quantile matching. Result We assessed our model by converting C1 scores (e.g. Mini‐Mental State Examination) to C2 scores (e.g. Montreal Cognitive Assessment). We trained our method on 18092 cognitively normal individuals with either a C1 or C2 score observed. The method was validated on a set 420 individuals with both scores observed using the Hellinger and Total Variation probability metrics to compare conversion distributions. Both methods outperformed prior methods in describing intrinsic variability (Table 1), which is the difference of scores from subsequent visits. Additionally, the non‐parametric model outperformed the others in matching the extrinsic variability, which is the difference of scores for individuals with multiple same day tests used to verify a model is capturing the conversion uncertainty (Table 1; Figures 1,2,3). Conclusion Our framework provides a statistically grounded approach to convert scores with uncertainty, a significant step towards understanding cognitive progression in settings where different neuropsychological batteries are used over time. This work establishes groundwork for future study including detection of Mild Cognitive Impairment when multiple testing batteries are used during an individual’s lifetime.
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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 | 0.099 | 0.233 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".