Estimation of planned and unplanned missing individual scores in longitudinal designs using continuous-time state-space models

Latent change score (LCS) models within a continuous-time state-space modeling framework provide a convenient statistical approach for analyzing developmental data. In this study, we evaluate the robustness of such an approach in the context of accelerated longitudinal designs (ALDs). ALDs are espec...

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Detalhes bibliográficos
Autores: Martínez-Huertas, José Ángel, Estrada Alonso, Eduardo, Olmos Albacete, Ricardo
Tipo de documento: artigo
Data de publicação:2024
País:España
Recursos:Universidad Autónoma de Madrid
Repositório:Biblos-e Archivo. Repositorio Institucional de la UAM
Idioma:inglês
OAI Identifier:oai:repositorio.uam.es:10486/731361
Acesso em linha:https://hdl.handle.net/10486/731361
https://dx.doi.org/10.1037/met0000664
Access Level:Acceso aberto
Palavra-chave:latent change score models
state-space modeling
continuous-time modeling
Kalman scores
missing data imputation
Psicología
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spelling Estimation of planned and unplanned missing individual scores in longitudinal designs using continuous-time state-space modelsMartínez-Huertas, José ÁngelEstrada Alonso, EduardoOlmos Albacete, Ricardolatent change score modelsstate-space modelingcontinuous-time modelingKalman scoresmissing data imputationPsicologíaLatent change score (LCS) models within a continuous-time state-space modeling framework provide a convenient statistical approach for analyzing developmental data. In this study, we evaluate the robustness of such an approach in the context of accelerated longitudinal designs (ALDs). ALDs are especially interesting because they imply a very high rate of planned data missingness. Additionally, most longitudinal studies present unexpected participant attrition leading to unplanned missing data. Therefore, in ALDs, both sources of data missingness are combined. Previous research has shown that ALDs for developmental research allow recovering the population generating process. However, it is unknown how participant attrition impacts the model estimates. We have three goals: (a) to evaluate the robustness of the group-level parameter estimates in scenarios with empirically plausible unplanned data missingness; (b) to evaluate the performance of Kalman scores (KS) imputations for individual data points that were expected but unobserved; and (c) to evaluate the performance of KS imputations for individual data points that were outside the age ranged observed for each case (i.e., to estimate the individual trajectories for the complete age range under study). In general, results showed lack of bias in the simulated conditions. The variability of the estimates increased with lower sample sizes and higher missingness severity. Similarly, we found very accurate estimates of individual scores for both planned and unplanned missing data points. These results are very important for applied practitioners in terms of forecasting and making individual-level decisions. R code is provided to facilitate its implementation by applied researchersThis work was funded by the Ministry of Science and Innovation of Spain (ref. PID2019-107570GA-I00 / AEI / doi:10.13039/501100011033), granted to EEAmerican Psychological AssociationDepartamento de Psicología Social y MetodologíaFacultad de PsicologíaAgencia Estatal de Investigación20242024-01-01research articlehttp://purl.org/coar/resource_type/c_2df8fbb1AMhttp://purl.org/coar/version/c_ab4af688f83e57aainfo:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/10486/731361https://dx.doi.org/10.1037/met000066438753382reponame:Biblos-e Archivo. Repositorio Institucional de la UAMinstname:Universidad Autónoma de MadridInglésengopen accesshttp://purl.org/coar/access_right/c_abf2info:eu-repo/semantics/openAccessoai:repositorio.uam.es:10486/7313612026-06-23T12:46:27Z
dc.title.none.fl_str_mv Estimation of planned and unplanned missing individual scores in longitudinal designs using continuous-time state-space models
title Estimation of planned and unplanned missing individual scores in longitudinal designs using continuous-time state-space models
spellingShingle Estimation of planned and unplanned missing individual scores in longitudinal designs using continuous-time state-space models
Martínez-Huertas, José Ángel
latent change score models
state-space modeling
continuous-time modeling
Kalman scores
missing data imputation
Psicología
title_short Estimation of planned and unplanned missing individual scores in longitudinal designs using continuous-time state-space models
title_full Estimation of planned and unplanned missing individual scores in longitudinal designs using continuous-time state-space models
title_fullStr Estimation of planned and unplanned missing individual scores in longitudinal designs using continuous-time state-space models
title_full_unstemmed Estimation of planned and unplanned missing individual scores in longitudinal designs using continuous-time state-space models
title_sort Estimation of planned and unplanned missing individual scores in longitudinal designs using continuous-time state-space models
dc.creator.none.fl_str_mv Martínez-Huertas, José Ángel
Estrada Alonso, Eduardo
Olmos Albacete, Ricardo
author Martínez-Huertas, José Ángel
author_facet Martínez-Huertas, José Ángel
Estrada Alonso, Eduardo
Olmos Albacete, Ricardo
author_role author
author2 Estrada Alonso, Eduardo
Olmos Albacete, Ricardo
author2_role author
author
dc.contributor.none.fl_str_mv Departamento de Psicología Social y Metodología
Facultad de Psicología
Agencia Estatal de Investigación
dc.subject.none.fl_str_mv latent change score models
state-space modeling
continuous-time modeling
Kalman scores
missing data imputation
Psicología
topic latent change score models
state-space modeling
continuous-time modeling
Kalman scores
missing data imputation
Psicología
description Latent change score (LCS) models within a continuous-time state-space modeling framework provide a convenient statistical approach for analyzing developmental data. In this study, we evaluate the robustness of such an approach in the context of accelerated longitudinal designs (ALDs). ALDs are especially interesting because they imply a very high rate of planned data missingness. Additionally, most longitudinal studies present unexpected participant attrition leading to unplanned missing data. Therefore, in ALDs, both sources of data missingness are combined. Previous research has shown that ALDs for developmental research allow recovering the population generating process. However, it is unknown how participant attrition impacts the model estimates. We have three goals: (a) to evaluate the robustness of the group-level parameter estimates in scenarios with empirically plausible unplanned data missingness; (b) to evaluate the performance of Kalman scores (KS) imputations for individual data points that were expected but unobserved; and (c) to evaluate the performance of KS imputations for individual data points that were outside the age ranged observed for each case (i.e., to estimate the individual trajectories for the complete age range under study). In general, results showed lack of bias in the simulated conditions. The variability of the estimates increased with lower sample sizes and higher missingness severity. Similarly, we found very accurate estimates of individual scores for both planned and unplanned missing data points. These results are very important for applied practitioners in terms of forecasting and making individual-level decisions. R code is provided to facilitate its implementation by applied researchers
publishDate 2024
dc.date.none.fl_str_mv 2024
2024-01-01
dc.type.none.fl_str_mv research article
http://purl.org/coar/resource_type/c_2df8fbb1
AM
http://purl.org/coar/version/c_ab4af688f83e57aa
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/10486/731361
https://dx.doi.org/10.1037/met0000664
38753382
url https://hdl.handle.net/10486/731361
https://dx.doi.org/10.1037/met0000664
identifier_str_mv 38753382
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv American Psychological Association
publisher.none.fl_str_mv American Psychological Association
dc.source.none.fl_str_mv reponame:Biblos-e Archivo. Repositorio Institucional de la UAM
instname:Universidad Autónoma de Madrid
instname_str Universidad Autónoma de Madrid
reponame_str Biblos-e Archivo. Repositorio Institucional de la UAM
collection Biblos-e Archivo. Repositorio Institucional de la UAM
repository.name.fl_str_mv
repository.mail.fl_str_mv
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