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Statistical methods for dementia risk prediction and recommendations for future work: A systematic review.

Abstract : 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.
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https://www.hal.inserm.fr/inserm-02417286
Contributor : Isabelle Carrière <>
Submitted on : Wednesday, December 18, 2019 - 10:19:07 AM
Last modification on : Thursday, December 19, 2019 - 2:10:18 AM
Document(s) archivé(s) le : Thursday, March 19, 2020 - 4:35:32 PM

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  • HAL Id : inserm-02417286, version 1

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Jantje Goerdten, Iva Čukic, Samuel Danso, Isabelle Carrière, Graciela Terrera Muniz. Statistical methods for dementia risk prediction and recommendations for future work: A systematic review.. Alzheimer's & Dementia: Translational Research & Clinical Interventions, Elsevier, 2019, 5, pp.563-569. ⟨inserm-02417286⟩

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