ArticleResearch square2026
Separating ECog scale-score information from dyad availability: observation-process contributions to dyadic prediction of cognitive impairment in ADNI.
Article in Research square, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: Self- and informant-reported cognitive concerns are common, but their apparent incremental discriminative contribution can be inflated when missingness is structured by study protocol and assessment era. We tested the hypothesis that dyadic ECog would improve discrimination beyond conventional predictors, but that this increment would be attenuated after accounting for dyad availability and protocol-structured missingness. Methods: We included 1,643 cognitively unimpaired (CU) participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI; 221 events: 213 mild cognitive impairment [MCI], 8 Alzheimer's disease [AD] dementia) and fit nested cross-validated elastic-net Cox models with fold-wise imputation. Results: The conventional model (M2) concordance index (C-index) was 0.680, but fell to 0.649 when missingness indicators were removed. M3 (M2 plus dyadic Everyday Cognition (ECog) mean and dyad availability) reached 0.758; after accounting for dyad availability, ECog scale scores showed a modest contribution beyond availability (ΔC = 0.026, 95% confidence interval (CI) 0.007 to 0.045), whereas the score-only comparison provided limited evidence for an independent contribution (ΔC = 0.010, 95% CI -0.004 to 0.024). In an inverse-probability-of-censoring-weighted (IPCW) sensitivity analysis, ΔC was 0.105 (95% CI 0.050 to 0.174). Conclusions: In this ADNI analysis, missingness-indicator imputation may increase the apparent discriminative contribution of dyadic measures; much of the observed gain was associated with dyad availability, and limited evidence was found for an independent contribution from ECog scale scores.
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