Sensitivity analysis for time-to-event data accounting for intra-individual variability in time-varying covariates with missing data

Autores
Liaqat, Madiha; Chiapella, Luciana Carla; Mishra, Pradeep; Emam, Walid; Tashkandy, Yusra; Matuka, Adelajda
Año de publicación
2025
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
The Delta-Adjusted (DA) approach in multiple imputation (MI) is applied under several key assumptions in the Cox hazard model, where two time-dependent covariates have missing observations.Missingness in these covariates is assumed to be not missing at random (NMAR) and is modeledthrough delta adjustments, with different delta values specified to capture deviations from the missing at random (MAR) assumption. Within the MI framework, missing values are imputed under various plausible missingness scenarios while preserving the relationship between time-dependent covariates and the event-time outcome. Event-time dependence is accounted for by assuming that missingness in covariates is influenced by an individual’s treatment response or disease progression, thereby capturing intra-individual variability. Compared to other sensitivity analysis techniques, DA under MI explicitly adjusts imputed values using delta shifts, providing a structured approach to handling NMAR data. Unlike traditional methods that rely on pattern-mixture or selection models without direct imputation, DA generates multiple datasets with controlled sensitivity adjustments, ensuring a better variability assessment. Additionally, DA allows flexible assumptions regarding loss to follow-up (FU) and event occurrence through delta values, whereas other methods often rely on fixed assumptions about missingness. Its results are more interpretable, providing sensitivity bounds for treatment effects under different missing data scenarios.
Fil: Liaqat, Madiha. University of the Punjab; Pakistán
Fil: Chiapella, Luciana Carla. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Farmacologia; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay; Argentina
Fil: Mishra, Pradeep. College Of Agriculture, Jnkvv, Rewa, India; India
Fil: Emam, Walid. King Saud University; Arabia Saudita
Fil: Tashkandy, Yusra. King Saud University; Arabia Saudita
Fil: Matuka, Adelajda. Universidad de Bologna; Italia
Materia
Event time data
Multiple imputation
Sensitivity analysis
Delta adjustment
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/279580
Acceder
id CONICETDig_1e87bcfea5f552b6fb5b55ab7dd5f174
oai_identifier_str oai:ri.conicet.gov.ar:11336/279580
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Sensitivity analysis for time-to-event data accounting for intra-individual variability in time-varying covariates with missing dataLiaqat, MadihaChiapella, Luciana CarlaMishra, PradeepEmam, WalidTashkandy, YusraMatuka, AdelajdaEvent time dataMultiple imputationSensitivity analysisDelta adjustmenthttps://purl.org/becyt/ford/1.1https://purl.org/becyt/ford/1The Delta-Adjusted (DA) approach in multiple imputation (MI) is applied under several key assumptions in the Cox hazard model, where two time-dependent covariates have missing observations.Missingness in these covariates is assumed to be not missing at random (NMAR) and is modeledthrough delta adjustments, with different delta values specified to capture deviations from the missing at random (MAR) assumption. Within the MI framework, missing values are imputed under various plausible missingness scenarios while preserving the relationship between time-dependent covariates and the event-time outcome. Event-time dependence is accounted for by assuming that missingness in covariates is influenced by an individual’s treatment response or disease progression, thereby capturing intra-individual variability. Compared to other sensitivity analysis techniques, DA under MI explicitly adjusts imputed values using delta shifts, providing a structured approach to handling NMAR data. Unlike traditional methods that rely on pattern-mixture or selection models without direct imputation, DA generates multiple datasets with controlled sensitivity adjustments, ensuring a better variability assessment. Additionally, DA allows flexible assumptions regarding loss to follow-up (FU) and event occurrence through delta values, whereas other methods often rely on fixed assumptions about missingness. Its results are more interpretable, providing sensitivity bounds for treatment effects under different missing data scenarios.Fil: Liaqat, Madiha. University of the Punjab; PakistánFil: Chiapella, Luciana Carla. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Farmacologia; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay; ArgentinaFil: Mishra, Pradeep. College Of Agriculture, Jnkvv, Rewa, India; IndiaFil: Emam, Walid. King Saud University; Arabia SauditaFil: Tashkandy, Yusra. King Saud University; Arabia SauditaFil: Matuka, Adelajda. Universidad de Bologna; ItaliaNatural Areas Association2025-07info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/279580Liaqat, Madiha; Chiapella, Luciana Carla; Mishra, Pradeep; Emam, Walid; Tashkandy, Yusra; et al.; Sensitivity analysis for time-to-event data accounting for intra-individual variability in time-varying covariates with missing data; Natural Areas Association; Scientific Reports; 15; 1; 7-20252045-2322CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.nature.com/articles/s41598-025-09599-3info:eu-repo/semantics/altIdentifier/doi/10.1038/s41598-025-09599-3info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-nd/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2026-02-26T10:05:29Zoai:ri.conicet.gov.ar:11336/279580instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982026-02-26 10:05:30.156CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Sensitivity analysis for time-to-event data accounting for intra-individual variability in time-varying covariates with missing data
title Sensitivity analysis for time-to-event data accounting for intra-individual variability in time-varying covariates with missing data
spellingShingle Sensitivity analysis for time-to-event data accounting for intra-individual variability in time-varying covariates with missing data
Liaqat, Madiha
Event time data
Multiple imputation
Sensitivity analysis
Delta adjustment
title_short Sensitivity analysis for time-to-event data accounting for intra-individual variability in time-varying covariates with missing data
title_full Sensitivity analysis for time-to-event data accounting for intra-individual variability in time-varying covariates with missing data
title_fullStr Sensitivity analysis for time-to-event data accounting for intra-individual variability in time-varying covariates with missing data
title_full_unstemmed Sensitivity analysis for time-to-event data accounting for intra-individual variability in time-varying covariates with missing data
title_sort Sensitivity analysis for time-to-event data accounting for intra-individual variability in time-varying covariates with missing data
dc.creator.none.fl_str_mv Liaqat, Madiha
Chiapella, Luciana Carla
Mishra, Pradeep
Emam, Walid
Tashkandy, Yusra
Matuka, Adelajda
author Liaqat, Madiha
author_facet Liaqat, Madiha
Chiapella, Luciana Carla
Mishra, Pradeep
Emam, Walid
Tashkandy, Yusra
Matuka, Adelajda
author_role author
author2 Chiapella, Luciana Carla
Mishra, Pradeep
Emam, Walid
Tashkandy, Yusra
Matuka, Adelajda
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv Event time data
Multiple imputation
Sensitivity analysis
Delta adjustment
topic Event time data
Multiple imputation
Sensitivity analysis
Delta adjustment
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.1
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv The Delta-Adjusted (DA) approach in multiple imputation (MI) is applied under several key assumptions in the Cox hazard model, where two time-dependent covariates have missing observations.Missingness in these covariates is assumed to be not missing at random (NMAR) and is modeledthrough delta adjustments, with different delta values specified to capture deviations from the missing at random (MAR) assumption. Within the MI framework, missing values are imputed under various plausible missingness scenarios while preserving the relationship between time-dependent covariates and the event-time outcome. Event-time dependence is accounted for by assuming that missingness in covariates is influenced by an individual’s treatment response or disease progression, thereby capturing intra-individual variability. Compared to other sensitivity analysis techniques, DA under MI explicitly adjusts imputed values using delta shifts, providing a structured approach to handling NMAR data. Unlike traditional methods that rely on pattern-mixture or selection models without direct imputation, DA generates multiple datasets with controlled sensitivity adjustments, ensuring a better variability assessment. Additionally, DA allows flexible assumptions regarding loss to follow-up (FU) and event occurrence through delta values, whereas other methods often rely on fixed assumptions about missingness. Its results are more interpretable, providing sensitivity bounds for treatment effects under different missing data scenarios.
Fil: Liaqat, Madiha. University of the Punjab; Pakistán
Fil: Chiapella, Luciana Carla. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Farmacologia; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay; Argentina
Fil: Mishra, Pradeep. College Of Agriculture, Jnkvv, Rewa, India; India
Fil: Emam, Walid. King Saud University; Arabia Saudita
Fil: Tashkandy, Yusra. King Saud University; Arabia Saudita
Fil: Matuka, Adelajda. Universidad de Bologna; Italia
description The Delta-Adjusted (DA) approach in multiple imputation (MI) is applied under several key assumptions in the Cox hazard model, where two time-dependent covariates have missing observations.Missingness in these covariates is assumed to be not missing at random (NMAR) and is modeledthrough delta adjustments, with different delta values specified to capture deviations from the missing at random (MAR) assumption. Within the MI framework, missing values are imputed under various plausible missingness scenarios while preserving the relationship between time-dependent covariates and the event-time outcome. Event-time dependence is accounted for by assuming that missingness in covariates is influenced by an individual’s treatment response or disease progression, thereby capturing intra-individual variability. Compared to other sensitivity analysis techniques, DA under MI explicitly adjusts imputed values using delta shifts, providing a structured approach to handling NMAR data. Unlike traditional methods that rely on pattern-mixture or selection models without direct imputation, DA generates multiple datasets with controlled sensitivity adjustments, ensuring a better variability assessment. Additionally, DA allows flexible assumptions regarding loss to follow-up (FU) and event occurrence through delta values, whereas other methods often rely on fixed assumptions about missingness. Its results are more interpretable, providing sensitivity bounds for treatment effects under different missing data scenarios.
publishDate 2025
dc.date.none.fl_str_mv 2025-07
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/279580
Liaqat, Madiha; Chiapella, Luciana Carla; Mishra, Pradeep; Emam, Walid; Tashkandy, Yusra; et al.; Sensitivity analysis for time-to-event data accounting for intra-individual variability in time-varying covariates with missing data; Natural Areas Association; Scientific Reports; 15; 1; 7-2025
2045-2322
CONICET Digital
CONICET
url http://hdl.handle.net/11336/279580
identifier_str_mv Liaqat, Madiha; Chiapella, Luciana Carla; Mishra, Pradeep; Emam, Walid; Tashkandy, Yusra; et al.; Sensitivity analysis for time-to-event data accounting for intra-individual variability in time-varying covariates with missing data; Natural Areas Association; Scientific Reports; 15; 1; 7-2025
2045-2322
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://www.nature.com/articles/s41598-025-09599-3
info:eu-repo/semantics/altIdentifier/doi/10.1038/s41598-025-09599-3
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Natural Areas Association
publisher.none.fl_str_mv Natural Areas Association
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
_version_ 1858305171582353408
score 13.176822

Publicaciones similares