• Reflections on dynamic predict...

Treffer: Reflections on dynamic prediction of Alzheimer's disease: advancements in modeling longitudinal outcomes and time-to-event data.

Title:
Reflections on dynamic prediction of Alzheimer's disease: advancements in modeling longitudinal outcomes and time-to-event data.
Authors:
Chen D; School of Public Health, Shanxi Medical University, Taiyuan, 030001, China., Zhang M; Academy of Medical Sciences, Shanxi Medical University, Taiyuan, 030001, China., Han H; School of Basic Medical Sciences, Shanxi Medical University, Taiyuan, 030001, China., Wen Y; Department of Statistics, University of Auckland, Auckland, 1010, New Zealand., Yu H; School of Public Health, Shanxi Medical University, Taiyuan, 030001, China. yu@sxmu.edu.cn.; Shanxi Provincial Key Laboratory of Major Diseases Risk Assessment, Taiyuan, 030001, China. yu@sxmu.edu.cn.; MOE Key Laboratory of Coal Environmental Pathogenicity and Prevention, Shanxi Medical University, Taiyuan, 030001, China. yu@sxmu.edu.cn.
Source:
BMC medical research methodology [BMC Med Res Methodol] 2025 Jul 17; Vol. 25 (1), pp. 175. Date of Electronic Publication: 2025 Jul 17.
Publication Type:
Journal Article; Systematic Review
Language:
English
Journal Info:
Publisher: BioMed Central Country of Publication: England NLM ID: 100968545 Publication Model: Electronic Cited Medium: Internet ISSN: 1471-2288 (Electronic) Linking ISSN: 14712288 NLM ISO Abbreviation: BMC Med Res Methodol Subsets: MEDLINE
Imprint Name(s):
Original Publication: London : BioMed Central, [2001-
MeSH Terms:
Alzheimer Disease*/diagnosis , Predictive Learning Models* , Deep Learning* , Models, Statistical*, Humans ; Prognosis ; Risk Assessment/methods
References:
Alzheimers Dement. 2023 Dec;19(12):5620-5631. (PMID: 37294682)
Dement Geriatr Cogn Dis Extra. 2013 Sep 28;3(1):320-32. (PMID: 24174927)
Stat Med. 2011 May 30;30(12):1366-80. (PMID: 21337596)
Stat Med. 2023 May 10;42(10):1492-1511. (PMID: 36805635)
BMC Med Res Methodol. 2022 Sep 19;22(1):245. (PMID: 36123621)
JAMA Psychiatry. 2017 May 1;74(5):528-534. (PMID: 28355471)
BMC Med Res Methodol. 2021 Oct 17;21(1):216. (PMID: 34657597)
Annu Int Conf IEEE Eng Med Biol Soc. 2024 Jul;2024:1-4. (PMID: 40039105)
Alzheimers Dement. 2023 Feb;19(2):658-670. (PMID: 35652476)
Biom J. 2019 May;61(3):514-534. (PMID: 30221403)
Artif Intell Med. 2025 Jul;165:103140. (PMID: 40305919)
Ageing Res Rev. 2024 Nov;101:102497. (PMID: 39293530)
Comput Stat Data Anal. 2019 Jan;129:14-29. (PMID: 30559575)
Front Hum Neurosci. 2025 Mar 21;19:1552178. (PMID: 40191032)
Int J Epidemiol. 2021 Nov 10;50(5):1731-1743. (PMID: 33729514)
Neurology. 2023 Jan 17;100(3):e297-e307. (PMID: 36220593)
Stat Med. 2012 Aug 30;31(19):2086-97. (PMID: 22415871)
Curr Alzheimer Res. 2023;20(2):89-97. (PMID: 37246322)
Stat Med. 2022 Jul 10;41(15):2894-2907. (PMID: 35347750)
Stat Methods Med Res. 2019 Dec;28(12):3649-3666. (PMID: 30463497)
BMC Med Res Methodol. 2024 Aug 6;24(1):172. (PMID: 39107693)
Nat Commun. 2023 Oct 24;14(1):6747. (PMID: 37875471)
Stat Med. 2016 Jun 15;35(13):2167-82. (PMID: 26748812)
Stat Med. 2019 Oct 30;38(24):4804-4818. (PMID: 31386218)
Alzheimers Dement. 2007 Jan;3(1):40-53. (PMID: 19595916)
Stat Med. 2022 Aug 15;41(18):3547-3560. (PMID: 35574725)
Stat Med. 2022 Jan 30;41(2):356-373. (PMID: 34726280)
Biometrics. 2022 Jun;78(2):435-447. (PMID: 33501651)
Stat Methods Med Res. 2019 Feb;28(2):327-342. (PMID: 28750578)
Biometrics. 2020 Dec;76(4):1109-1119. (PMID: 32010968)
J Psychiatr Res. 2023 Oct;166:92-99. (PMID: 37757706)
Nat Med. 2024 Oct;30(10):2977-2989. (PMID: 38965435)
Biometrics. 2010 Sep;66(3):983-7; discussion 987-91. (PMID: 20849547)
Stat Methods Med Res. 2021 Jan;30(1):99-111. (PMID: 32726189)
BMC Med Res Methodol. 2016 Sep 07;16(1):117. (PMID: 27604810)
Ann Appl Stat. 2023 Sep;17(3):2574-2595. (PMID: 37719893)
Stat Biosci. 2017;9(2):489-503. (PMID: 29225713)
Curr Opin Psychiatry. 2019 Mar;32(2):123-129. (PMID: 30557268)
Int J Epidemiol. 2015 Feb;44(1):334-44. (PMID: 25604450)
Diagn Progn Res. 2020 Jul 09;4:9. (PMID: 32671229)
Stat Med. 2021 Dec 30;40(30):6855-6872. (PMID: 34649301)
Alzheimers Dement. 2025 Mar;21(3):e70055. (PMID: 40042504)
Stat Med. 2017 Sep 30;36(22):3560-3572. (PMID: 28664662)
Biometrics. 2017 Mar;73(1):83-93. (PMID: 27438160)
Stat Med. 2015 Jun 30;34(14):2181-95. (PMID: 24634327)
IEEE J Biomed Health Inform. 2020 Feb;24(2):424-436. (PMID: 31331898)
Stat (Int Stat Inst). 2020;9(1):. (PMID: 34262756)
Grant Information:
202403021221161 Natural Science Foundation of Shanxi Province; 82173632 National Natural Science Foundation of China; 82273742 National Natural Science Foundation of China
Contributed Indexing:
Keywords: Alzheimer’s disease; Dynamic survival analysis; Longitudinal data; Survival outcome
Entry Date(s):
Date Created: 20250717 Date Completed: 20250822 Latest Revision: 20250822
Update Code:
20250827
PubMed Central ID:
PMC12273041
DOI:
10.1186/s12874-025-02618-x
PMID:
40676602
Database:
MEDLINE

Weitere Informationen

Background: Individualized prediction of health outcomes supports clinical medicine and decision making. Our primary objective was to offer a comprehensive survey of methods for the dynamic prediction of Alzheimer's disease (AD), encompassing both conventional statistical methods and deep learning techniques.
Methods: Articles were sourced from PubMed, Embase and Web of Science databases using keywords related to dynamic prediction of AD. A set of criteria was developed to identify included studies. The correlation information for the construction of models was extracted.
Results: We identified four methodological frameworks for dynamic prediction from 18 studies with two-stage model (n = 3), joint model (n = 11), landmark model (n = 2) and deep learning (n = 2). We reported and summarized the specific construction of models and their applications.
Conclusions: Each framework possesses distinctive principles and attendant benefits. The dynamic prediction models excel in predicting the prognosis of individual patients in a real-time manner, surpassing the limitations of traditional baseline-only prediction models. Future work should consider various data types, complex longitudinal data, missing data, assumption violations, survival outcomes, and interpretability of models.
(© 2025. The Author(s).)

Declarations. Ethical approval and consent to participate: This article does not contain any studies with human participants or animals performed by any of the authors. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests.