Open AccessStatistical methods for dementia risk prediction and recommendations for future work: A systematic review
Author(s) -
Goerdten Jantje,
Čukić Iva,
Danso Samuel O.,
Carrière Isabelle,
MunizTerrera Graciela
Publication year - 2019
Publication title -
alzheimer's and dementia: translational research and clinical interventions
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.49
H-Index - 30
ISSN - 2352-8737
DOI - 10.1016/j.trci.2019.08.001
Subject(s) - dementia , logistic regression , computer science , strengths and weaknesses , regression analysis , data science , systematic review , regression , machine learning , management science , artificial intelligence , risk analysis (engineering) , medline , psychology , statistics , medicine , engineering , disease , mathematics , social psychology , pathology , political science , law
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.