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44/100

✓ Peer-reviewed alzhub usefulness score 44 out of 100. Digital health tools

Topic: Risk, prevention and care in population studies

Introduction Numerous dementia risk prediction models have been developed in the past decade. However, methodological limitations of the analytical tools used may hamper their ability to generate reliable dementia risk scores. We aim to review the used methodologies. Methods We systematically reviewed the literature from March 2014 to September 2018 for publications presenting a dementia risk prediction model. We critically discuss the analytical techniques used in the literature. Results In total 137 publications were included in the qualitative synthesis. Three techniques were identified as the most commonly used methodologies: machine learning, logistic regression, and Cox regression. Discussion We identified three major methodological weaknesses: (1) over-reliance on one data source, (2) poor verification of statistical assumptions of Cox and logistic regression, and (3) lack of validation. The use of larger and more diverse data sets is recommended. Assumptions should be tested thoroughly, and actions should be taken if deviations are detected.

Key findings

Results In total 137 publications were included in the qualitative synthesis.

Why it matters

This systematic review of 137 publications on dementia risk prediction models identified machine learning, logistic regression, and Cox regression as the most commonly used methodologies. Three major methodological weaknesses were found: over-reliance on one data source, poor verification of statistical assumptions, and lack of validation.

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Study details

Evidence strengthLow

How this study scored 44/100

Published in a leading specialty journal. Scored zero for study design, sample size, effect reported, studied in people and randomised / blinded because the record does not state it.

Component	Points	Why
Journal tier	9 / 10	Tier 2 — leading specialty journal (Alzheimer's & dementia (New York, N. Y.))
Journal's citation record	1 / 4	Alzheimer s & Dementia Translational Research & Clinical Interventions: journal h-index 84 (OpenAlex, 2026-10-29)
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	10 / 10	Peer-reviewed. Published as an article in a named peer-reviewed journal.
How recent	2 / 5	2019 — within 10 year(s) of the newest study here
Sample size	0 / 4	Participant count not stated — scores zero (not stated in the record — scored 0)
Effect reported	0 / 3	No effect size reported — scores zero (not stated in the record — scored 0)
Studied in people	0 / 3	Not stated whether this was studied in people — scores zero (not stated in the record — scored 0)
Randomised / blinded	0 / 2	No randomisation or blinding stated — scores zero (not stated in the record — scored 0)
Times cited	4 / 7	Cited 55 times (OpenAlex, 2026-10-10)
Citations per year	2 / 5	6.1 citations a year over 9 year(s) since 2019
Impact for its field and year	2 / 6	Cited 1.1× the average for papers of its field and year (OpenAlex FWCI)
Cited by studies here	2 / 6	Cited by 3 other studies in this library
Still being cited	2 / 4	Cited 20 times in 2024–2026 (2026 still in progress; OpenAlex)
Senior author's track record	2 / 4	Last-listed author Graciela Muniz Terrera: h-index 44 (OpenAlex, whole career)
Institution's track record	2 / 3	Inserm: institution h-index 1,988 (OpenAlex)
Multi-centre collaboration	1 / 3	OpenAlex lists 3 institution(s) in 2 countries
Indexed in PubMed	2 / 2	Indexed in PubMed (PMID 31646170)
Free to read	2 / 2	Free to read
Total	44 / 100	—

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Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).

Cite this study APA, Vancouver, BibTeX · download .bib or .ris

Built from the record alzhub holds for this paper, not looked up. Volume, issue and page numbers are not held in the record. Check the citation against the original before you use it.

APA 7th

Goerdten, J., Čukić, I., Danso, S. O., Carrière, I., & Muniz-Terrera, G. (2019). Statistical methods for dementia risk prediction and recommendations for future work: A systematic review. Alzheimer's & dementia (New York, N. Y.). https://doi.org/10.1016/j.trci.2019.08.001

Vancouver

Goerdten J, Čukić I, Danso SO, Carrière I, Muniz-Terrera G. Statistical methods for dementia risk prediction and recommendations for future work: A systematic review. Alzheimer's & dementia (New York, N. Y.). 2019. doi:10.1016/j.trci.2019.08.001. PMID: 31646170.

BibTeX

@article{goerdten2019-31646170,
  author  = {Goerdten, J. and Čukić, I. and Danso, S. O. and Carrière, I. and Muniz-Terrera, G.},
  title   = {{Statistical methods for dementia risk prediction and recommendations for future work: A systematic review}},
  journal = {Alzheimer's \& dementia (New York, N. Y.)},
  year    = {2019},
  doi     = {10.1016/j.trci.2019.08.001},
  pmid    = {31646170},
  url     = {https://doi.org/10.1016/j.trci.2019.08.001}
}

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