Estimating the longitudinal trajectory of cognitive function measurement using short‐term data with different disease stages: Application in Alzheimer's disease
Alzheimer's disease (AD) is a chronic neurodegenerative disease characterized by a gradual decline in cognitive function over a few decades. The Mini‐Mental State Examination (MMSE) is a widely used measure for evaluating global cognitive functioning. Characterizing the longitudinal trajectory...
Gespeichert in:
Veröffentlicht in: | Statistics in medicine 2022-09, Vol.41 (21), p.4200-4214 |
---|---|
Hauptverfasser: | , , , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
container_end_page | 4214 |
---|---|
container_issue | 21 |
container_start_page | 4200 |
container_title | Statistics in medicine |
container_volume | 41 |
creator | Hirakawa, Akihiro Sato, Hiroyuki Hanazawa, Ryoichi Suzuki, Keisuke |
description | Alzheimer's disease (AD) is a chronic neurodegenerative disease characterized by a gradual decline in cognitive function over a few decades. The Mini‐Mental State Examination (MMSE) is a widely used measure for evaluating global cognitive functioning. Characterizing the longitudinal trajectory of the MMSE in the population of interest is important to detect AD onset for preventive intervention. In this study, we formulate a new class of longitudinal trajectory modeling for MMSE from short‐term individual data based on an ordinary differential equation. The proposed method models the relationship between individual decline speed of MMSE and the average MMSE using the fractional polynomial function model and subsequently estimates the longitudinal trajectory of MMSE by solving the ordinary differential equation for the estimated model. The appropriate model for trajectory estimation is selected based on the proposed criterion for quantifying the goodness of trajectory fit. The accuracy of the trajectory estimation of the proposed method was demonstrated via simulation studies. The proposed method was successfully applied to MMSE data from the Japanese Alzheimer's Disease Neuroimaging Initiative study. |
doi_str_mv | 10.1002/sim.9504 |
format | Article |
fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_2681045054</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2707814184</sourcerecordid><originalsourceid>FETCH-LOGICAL-c3264-cb9620a3b0d4e50139a82e68a01f957e0431e5b5d920e6afac064093e2fff9c3</originalsourceid><addsrcrecordid>eNp10U1uFDEQBWArIhJDQOIIlrKATYey2-4fdqMogUiJWJB9y-Muz3jUbQ8uN9Gw4gg5A0fjJPQkIKRIWdXmq7d4j7G3As4EgPxAfjxrNagjthDQ1gVI3bxgC5B1XVS10C_ZK6ItgBBa1gv264KyH032Yc3zBvkQw9rnqffBDDwns0WbY9rz6LiN6-Cz_47cTcFmHwMf0dCUcMSQ-USHDNrElH__vM-YRt6bbPidzxvee-cwHVjvaX5CTtmskT7y5W43eGse4nzgy-HHBv2I6R39o6_ZsTMD4Zu_94TdXl7cnn8urr98ujpfXhe2lJUq7KqtJJhyBb1CDaJsTSOxagwI1-oaQZUC9Ur3rQSsjDMWKgVtidI519ryhL1_jN2l-G1Cyt3oyeIwmIBxok5WjQClQauZnj6h2zilubFZ1VA3QolG_Q-0KRIldN0uzVWnfSegO2zVzVt1h61mWjzSOz_g_lnXfb26efB_AEh9mhY</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2707814184</pqid></control><display><type>article</type><title>Estimating the longitudinal trajectory of cognitive function measurement using short‐term data with different disease stages: Application in Alzheimer's disease</title><source>Access via Wiley Online Library</source><creator>Hirakawa, Akihiro ; Sato, Hiroyuki ; Hanazawa, Ryoichi ; Suzuki, Keisuke</creator><creatorcontrib>Hirakawa, Akihiro ; Sato, Hiroyuki ; Hanazawa, Ryoichi ; Suzuki, Keisuke ; Japanese Alzheimer's Disease Neuroimaging Initiative</creatorcontrib><description>Alzheimer's disease (AD) is a chronic neurodegenerative disease characterized by a gradual decline in cognitive function over a few decades. The Mini‐Mental State Examination (MMSE) is a widely used measure for evaluating global cognitive functioning. Characterizing the longitudinal trajectory of the MMSE in the population of interest is important to detect AD onset for preventive intervention. In this study, we formulate a new class of longitudinal trajectory modeling for MMSE from short‐term individual data based on an ordinary differential equation. The proposed method models the relationship between individual decline speed of MMSE and the average MMSE using the fractional polynomial function model and subsequently estimates the longitudinal trajectory of MMSE by solving the ordinary differential equation for the estimated model. The appropriate model for trajectory estimation is selected based on the proposed criterion for quantifying the goodness of trajectory fit. The accuracy of the trajectory estimation of the proposed method was demonstrated via simulation studies. The proposed method was successfully applied to MMSE data from the Japanese Alzheimer's Disease Neuroimaging Initiative study.</description><identifier>ISSN: 0277-6715</identifier><identifier>EISSN: 1097-0258</identifier><identifier>DOI: 10.1002/sim.9504</identifier><language>eng</language><publisher>New York: Wiley Subscription Services, Inc</publisher><subject>Alzheimer's disease ; Cognitive ability ; longitudinal trajectory ; Mini‐Mental State Examination ; ordinary differential equation ; Ordinary differential equations ; short‐term individual data</subject><ispartof>Statistics in medicine, 2022-09, Vol.41 (21), p.4200-4214</ispartof><rights>2022 John Wiley & Sons Ltd.</rights><rights>2022 John Wiley & Sons, Ltd.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c3264-cb9620a3b0d4e50139a82e68a01f957e0431e5b5d920e6afac064093e2fff9c3</citedby><cites>FETCH-LOGICAL-c3264-cb9620a3b0d4e50139a82e68a01f957e0431e5b5d920e6afac064093e2fff9c3</cites><orcidid>0000-0003-2580-7460</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1002%2Fsim.9504$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1002%2Fsim.9504$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>314,780,784,1417,27924,27925,45574,45575</link.rule.ids></links><search><creatorcontrib>Hirakawa, Akihiro</creatorcontrib><creatorcontrib>Sato, Hiroyuki</creatorcontrib><creatorcontrib>Hanazawa, Ryoichi</creatorcontrib><creatorcontrib>Suzuki, Keisuke</creatorcontrib><creatorcontrib>Japanese Alzheimer's Disease Neuroimaging Initiative</creatorcontrib><title>Estimating the longitudinal trajectory of cognitive function measurement using short‐term data with different disease stages: Application in Alzheimer's disease</title><title>Statistics in medicine</title><description>Alzheimer's disease (AD) is a chronic neurodegenerative disease characterized by a gradual decline in cognitive function over a few decades. The Mini‐Mental State Examination (MMSE) is a widely used measure for evaluating global cognitive functioning. Characterizing the longitudinal trajectory of the MMSE in the population of interest is important to detect AD onset for preventive intervention. In this study, we formulate a new class of longitudinal trajectory modeling for MMSE from short‐term individual data based on an ordinary differential equation. The proposed method models the relationship between individual decline speed of MMSE and the average MMSE using the fractional polynomial function model and subsequently estimates the longitudinal trajectory of MMSE by solving the ordinary differential equation for the estimated model. The appropriate model for trajectory estimation is selected based on the proposed criterion for quantifying the goodness of trajectory fit. The accuracy of the trajectory estimation of the proposed method was demonstrated via simulation studies. The proposed method was successfully applied to MMSE data from the Japanese Alzheimer's Disease Neuroimaging Initiative study.</description><subject>Alzheimer's disease</subject><subject>Cognitive ability</subject><subject>longitudinal trajectory</subject><subject>Mini‐Mental State Examination</subject><subject>ordinary differential equation</subject><subject>Ordinary differential equations</subject><subject>short‐term individual data</subject><issn>0277-6715</issn><issn>1097-0258</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><recordid>eNp10U1uFDEQBWArIhJDQOIIlrKATYey2-4fdqMogUiJWJB9y-Muz3jUbQ8uN9Gw4gg5A0fjJPQkIKRIWdXmq7d4j7G3As4EgPxAfjxrNagjthDQ1gVI3bxgC5B1XVS10C_ZK6ItgBBa1gv264KyH032Yc3zBvkQw9rnqffBDDwns0WbY9rz6LiN6-Cz_47cTcFmHwMf0dCUcMSQ-USHDNrElH__vM-YRt6bbPidzxvee-cwHVjvaX5CTtmskT7y5W43eGse4nzgy-HHBv2I6R39o6_ZsTMD4Zu_94TdXl7cnn8urr98ujpfXhe2lJUq7KqtJJhyBb1CDaJsTSOxagwI1-oaQZUC9Ur3rQSsjDMWKgVtidI519ryhL1_jN2l-G1Cyt3oyeIwmIBxok5WjQClQauZnj6h2zilubFZ1VA3QolG_Q-0KRIldN0uzVWnfSegO2zVzVt1h61mWjzSOz_g_lnXfb26efB_AEh9mhY</recordid><startdate>20220920</startdate><enddate>20220920</enddate><creator>Hirakawa, Akihiro</creator><creator>Sato, Hiroyuki</creator><creator>Hanazawa, Ryoichi</creator><creator>Suzuki, Keisuke</creator><general>Wiley Subscription Services, Inc</general><scope>AAYXX</scope><scope>CITATION</scope><scope>K9.</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0003-2580-7460</orcidid></search><sort><creationdate>20220920</creationdate><title>Estimating the longitudinal trajectory of cognitive function measurement using short‐term data with different disease stages: Application in Alzheimer's disease</title><author>Hirakawa, Akihiro ; Sato, Hiroyuki ; Hanazawa, Ryoichi ; Suzuki, Keisuke</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3264-cb9620a3b0d4e50139a82e68a01f957e0431e5b5d920e6afac064093e2fff9c3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Alzheimer's disease</topic><topic>Cognitive ability</topic><topic>longitudinal trajectory</topic><topic>Mini‐Mental State Examination</topic><topic>ordinary differential equation</topic><topic>Ordinary differential equations</topic><topic>short‐term individual data</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Hirakawa, Akihiro</creatorcontrib><creatorcontrib>Sato, Hiroyuki</creatorcontrib><creatorcontrib>Hanazawa, Ryoichi</creatorcontrib><creatorcontrib>Suzuki, Keisuke</creatorcontrib><creatorcontrib>Japanese Alzheimer's Disease Neuroimaging Initiative</creatorcontrib><collection>CrossRef</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>MEDLINE - Academic</collection><jtitle>Statistics in medicine</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Hirakawa, Akihiro</au><au>Sato, Hiroyuki</au><au>Hanazawa, Ryoichi</au><au>Suzuki, Keisuke</au><aucorp>Japanese Alzheimer's Disease Neuroimaging Initiative</aucorp><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Estimating the longitudinal trajectory of cognitive function measurement using short‐term data with different disease stages: Application in Alzheimer's disease</atitle><jtitle>Statistics in medicine</jtitle><date>2022-09-20</date><risdate>2022</risdate><volume>41</volume><issue>21</issue><spage>4200</spage><epage>4214</epage><pages>4200-4214</pages><issn>0277-6715</issn><eissn>1097-0258</eissn><abstract>Alzheimer's disease (AD) is a chronic neurodegenerative disease characterized by a gradual decline in cognitive function over a few decades. The Mini‐Mental State Examination (MMSE) is a widely used measure for evaluating global cognitive functioning. Characterizing the longitudinal trajectory of the MMSE in the population of interest is important to detect AD onset for preventive intervention. In this study, we formulate a new class of longitudinal trajectory modeling for MMSE from short‐term individual data based on an ordinary differential equation. The proposed method models the relationship between individual decline speed of MMSE and the average MMSE using the fractional polynomial function model and subsequently estimates the longitudinal trajectory of MMSE by solving the ordinary differential equation for the estimated model. The appropriate model for trajectory estimation is selected based on the proposed criterion for quantifying the goodness of trajectory fit. The accuracy of the trajectory estimation of the proposed method was demonstrated via simulation studies. The proposed method was successfully applied to MMSE data from the Japanese Alzheimer's Disease Neuroimaging Initiative study.</abstract><cop>New York</cop><pub>Wiley Subscription Services, Inc</pub><doi>10.1002/sim.9504</doi><tpages>15</tpages><orcidid>https://orcid.org/0000-0003-2580-7460</orcidid></addata></record> |
fulltext | fulltext |
identifier | ISSN: 0277-6715 |
ispartof | Statistics in medicine, 2022-09, Vol.41 (21), p.4200-4214 |
issn | 0277-6715 1097-0258 |
language | eng |
recordid | cdi_proquest_miscellaneous_2681045054 |
source | Access via Wiley Online Library |
subjects | Alzheimer's disease Cognitive ability longitudinal trajectory Mini‐Mental State Examination ordinary differential equation Ordinary differential equations short‐term individual data |
title | Estimating the longitudinal trajectory of cognitive function measurement using short‐term data with different disease stages: Application in Alzheimer's disease |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-23T01%3A28%3A03IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Estimating%20the%20longitudinal%20trajectory%20of%20cognitive%20function%20measurement%20using%20short%E2%80%90term%20data%20with%20different%20disease%20stages:%20Application%20in%20Alzheimer's%20disease&rft.jtitle=Statistics%20in%20medicine&rft.au=Hirakawa,%20Akihiro&rft.aucorp=Japanese%20Alzheimer's%20Disease%20Neuroimaging%20Initiative&rft.date=2022-09-20&rft.volume=41&rft.issue=21&rft.spage=4200&rft.epage=4214&rft.pages=4200-4214&rft.issn=0277-6715&rft.eissn=1097-0258&rft_id=info:doi/10.1002/sim.9504&rft_dat=%3Cproquest_cross%3E2707814184%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2707814184&rft_id=info:pmid/&rfr_iscdi=true |