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Separating ECog scale-score information from dyad availability: observation-process contributions to dyadic prediction of cognitive impairment in ADNI
CHENG WKW.. Research Square, 2026-09-22
Topic: Brain imaging, connectivity and assessment
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). 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.
Key findings
- 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).
- 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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Study details
- Participants1,643
- Evidence strengthLow
How this study scored 16/100
Preprint — not yet peer-reviewed. It scored 31 on the same rubric, but non-peer-reviewed sources are capped at 16, so it shows as 16. Scored zero for study design, effect reported and randomised / blinded because the record does not state it. We have no data on times cited, citations per year, impact for its field and year, cited by studies here, still being cited, senior author's track record, institution's track record and multi-centre collaboration, so those were left out: it earned 19 of the 62 points we could judge it on, which is 31 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 2 / 10 | Tier 4 — not a peer-reviewed journal (Research Square) |
| Journal's citation record | 0 / 4 | Not a journal article, so the journal's record does not count |
| Study design | 0 / 12 | No study design recorded — scores zero, not guessed (not stated in the record — scored 0) |
| Strength of evidence | 1 / 5 | Rated “low” in our summary |
| Peer-review validation | 4 / 10 | Preprint — not yet peer-reviewed. Hosted on a preprint server. |
| How recent | 5 / 5 | 2026 — within 1 year(s) of the newest study here |
| Sample size | 3 / 4 | 1,643 participants |
| Effect reported | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| Studied in people | 3 / 3 | Studied in people (participant count stated) |
| Randomised / blinded | 0 / 2 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | — / 7 | No citation count (not found in OpenAlex) — left out of this score |
| Citations per year | — / 5 | No citation count or no year — left out of this score |
| Impact for its field and year | — / 6 | No field-weighted citation impact (OpenAlex has none yet) — left out of this score |
| Cited by studies here | — / 6 | Not yet checked against this library's citation graph — left out of this score |
| Still being cited | — / 4 | No yearly citation counts — left out of this score |
| Senior author's track record | — / 4 | Senior author's record not available — left out of this score |
| Institution's track record | — / 3 | No institution listed in OpenAlex — left out of this score |
| Multi-centre collaboration | — / 3 | Institutions not listed — left out of this score |
| Indexed in PubMed | 1 / 2 | Not in PubMed, but has a DOI |
| Free to read | 0 / 2 | Behind a paywall — scores zero |
| Points | 19 / 62 | Scored on 12 of 20 parts. We have no data for the rest, so they are left out: 19 of the 62 points we could judge it on is 31 out of 100. |
| Total | 31 / 100 | Capped to 16 because this is not a peer-reviewed article. |
What each of these 20 components measures, and what this score is not →
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APA 7th
CHENG, W. K. W. (2026). Separating ECog scale-score information from dyad availability: observation-process contributions to dyadic prediction of cognitive impairment in ADNI. Research Square. https://doi.org/10.21203/rs.3.rs-10884877/v3
Vancouver
CHENG WKW. Separating ECog scale-score information from dyad availability: observation-process contributions to dyadic prediction of cognitive impairment in ADNI. Research Square. 2026. doi:10.21203/rs.3.rs-10884877/v3.
BibTeX
@article{cheng2026-9af913f5,
author = {CHENG, W. K. W.},
title = {{Separating ECog scale-score information from dyad availability: observation-process contributions to dyadic prediction of cognitive impairment in ADNI}},
journal = {Research Square},
year = {2026},
doi = {10.21203/rs.3.rs-10884877/v3},
url = {https://doi.org/10.21203/rs.3.rs-10884877/v3}
}