Publicação
Joint Modelling of Longitudinal and Competing Risks Data in Clinical Research
| Resumo: | Joint modelling of longitudinal and survival data has received much attention in the recent years and is becoming increasingly used in clinical studies. When the longitudinal outcome and survival endpoints are associated, the many well-established models with different specifications proposed to analyse separately longitudinal and time-to-event outcomes are not suitable to analyse such data and a joint modelling approach is required. Although some joint models were adapted in order to allow for competing endpoints, this methodology has not been widely disseminated. The present study has as main objective to model jointly longitudinal and survival data in a competing risk context, discussing the different parameterisations of systematic implementations of these models in the R, using a real data set as an example for the comparison between the different model approaches. The relevance of this issue is associated with the need to draw attention of the users of this statistical software to the different interpretations of model parameters when fitting these models. To reinforce the relevance of these models in clinical research, we give an example of a data set on peritoneal dialysis that was analysed in this context, where death/transfer to haemodialysis was the event of interest and renal transplant was the competing event. Joint modelling results were also compared to separate analysis for these data. |
|---|---|
| Autores principais: | Teixeira , Laetitia |
| Outros Autores: | Sousa , Inês; Rodrigues , Anabela; Mendonça , Denisa |
| Assunto: | competing risks joint modelling longitudinal data peritoneal dialysis time-to-event data |
| Ano: | 2019 |
| País: | Portugal |
| Tipo de documento: | artigo |
| Tipo de acesso: | unknown |
| Instituição associada: | Instituto Nacional de Estatística |
| Idioma: | inglês |
| Origem: | REVSTAT-Statistical Journal |
| _version_ | 1869319184293298176 |
|---|---|
| author | Teixeira , Laetitia |
| author2 | Sousa , Inês Rodrigues , Anabela Mendonça , Denisa |
| author2_role | author author author |
| author_facet | Teixeira , Laetitia Sousa , Inês Rodrigues , Anabela Mendonça , Denisa |
| author_role | author |
| country_str | PT |
| creators_json_txt | [{\"Person.name\":\"Teixeira , Laetitia\"},{\"Person.name\":\"Sousa , Inês\"},{\"Person.name\":\"Rodrigues , Anabela\"},{\"Person.name\":\"Mendonça , Denisa\"}] |
| datacite.creators.creator.creatorName.fl_str_mv | Teixeira , Laetitia Sousa , Inês Rodrigues , Anabela Mendonça , Denisa |
| datacite.rights.fl_str_mv | http://purl.org/coar/access_right/c_abf2 |
| datacite.subjects.subject.fl_str_mv | competing risks joint modelling longitudinal data peritoneal dialysis time-to-event data |
| datacite.titles.title.fl_str_mv | Joint Modelling of Longitudinal and Competing Risks Data in Clinical Research |
| dc.creator.none.fl_str_mv | Teixeira , Laetitia Sousa , Inês Rodrigues , Anabela Mendonça , Denisa |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | https://doi.org/10.57805/revstat.v17i2.267 |
| dc.language.none.fl_str_mv | eng |
| dc.publisher.none.fl_str_mv | Statistics Portugal |
| dc.rights.none.fl_str_mv | http://purl.org/coar/access_right/c_abf2 |
| dc.source.none.fl_str_mv | REVSTAT-Statistical Journal; Vol. 17 No. 2 (2019): REVSTAT-Statistical Journal; 245-264 REVSTAT; Vol. 17 N.º 2 (2019): REVSTAT-Statistical Journal; 245-264 2183-0371 1645-6726 |
| dc.subject.none.fl_str_mv | competing risks joint modelling longitudinal data peritoneal dialysis time-to-event data |
| dc.title.fl_str_mv | Joint Modelling of Longitudinal and Competing Risks Data in Clinical Research |
| dc.type.none.fl_str_mv | http://purl.org/coar/resource_type/c_6501 |
| description | Joint modelling of longitudinal and survival data has received much attention in the recent years and is becoming increasingly used in clinical studies. When the longitudinal outcome and survival endpoints are associated, the many well-established models with different specifications proposed to analyse separately longitudinal and time-to-event outcomes are not suitable to analyse such data and a joint modelling approach is required. Although some joint models were adapted in order to allow for competing endpoints, this methodology has not been widely disseminated. The present study has as main objective to model jointly longitudinal and survival data in a competing risk context, discussing the different parameterisations of systematic implementations of these models in the R, using a real data set as an example for the comparison between the different model approaches. The relevance of this issue is associated with the need to draw attention of the users of this statistical software to the different interpretations of model parameters when fitting these models. To reinforce the relevance of these models in clinical research, we give an example of a data set on peritoneal dialysis that was analysed in this context, where death/transfer to haemodialysis was the event of interest and renal transplant was the competing event. Joint modelling results were also compared to separate analysis for these data. |
| dirty | 0 |
| eu_rights_str_mv | unknown |
| format | article |
| id | revstat_a7496ea514b658defefbc331200f39b4 |
| identifier.doi.fl_str_mv | https://doi.org/10.57805/revstat.v17i2.267 |
| inst_facet_str | urn:organizationAcronym:revstat-statistical journal{{{_:::_}}}Instituto Nacional de Estatística |
| instacron_str | REVSTAT-Statistical Journal |
| institution | Instituto Nacional de Estatística |
| instname_str | Instituto Nacional de Estatística |
| language | eng |
| network_acronym_str | revstat |
| network_name_str | REVSTAT-Statistical Journal |
| oai_identifier_str | oai:revstat:article/267 |
| organization_str_mv | urn:organizationAcronym:revstat-statistical journal |
| person_str_mv | Teixeira , Laetitia Sousa , Inês Rodrigues , Anabela Mendonça , Denisa |
| publishDate | 2019 |
| publisher.none.fl_str_mv | Statistics Portugal |
| repo_facet_str | urn:repositoryAcronym:revstat{{{_:::_}}}REVSTAT-Statistical Journal |
| reponame_str | REVSTAT-Statistical Journal |
| repository_id_str | urn:repositoryAcronym:revstat |
| service_str_mv | urn:repositoryAcronym:revstat |
| spelling | en-USJoint Modelling of Longitudinal and Competing Risks Data in Clinical ResearchTeixeira , LaetitiaSousa , InêsRodrigues , AnabelaMendonça , Denisacompeting risksjoint modellinglongitudinal dataperitoneal dialysistime-to-event dataCopyright (c) 2019 REVSTAT-Statistical Journalhttp://purl.org/coar/access_right/c_abf2https://creativecommons.org/licenses/by/4.0https://doi.org/10.57805/revstat.v17i2.267DOIoai:revstat:article/267OAIhttps://revstat.ine.pt/index.php/REVSTAT/article/view/267URLhttps://doi.org/10.57805/revstat.v17i2.267DOIhttps://revstat.ine.pt/index.php/REVSTAT/article/view/267/550URLHasVersion2019-04-22T00:00:00Zen-USJoint modelling of longitudinal and survival data has received much attention in the recent years and is becoming increasingly used in clinical studies. When the longitudinal outcome and survival endpoints are associated, the many well-established models with different specifications proposed to analyse separately longitudinal and time-to-event outcomes are not suitable to analyse such data and a joint modelling approach is required. Although some joint models were adapted in order to allow for competing endpoints, this methodology has not been widely disseminated. The present study has as main objective to model jointly longitudinal and survival data in a competing risk context, discussing the different parameterisations of systematic implementations of these models in the R, using a real data set as an example for the comparison between the different model approaches. The relevance of this issue is associated with the need to draw attention of the users of this statistical software to the different interpretations of model parameters when fitting these models. To reinforce the relevance of these models in clinical research, we give an example of a data set on peritoneal dialysis that was analysed in this context, where death/transfer to haemodialysis was the event of interest and renal transplant was the competing event. Joint modelling results were also compared to separate analysis for these data.Statistics Portugalapplication/pdfen-USREVSTAT-Statistical Journal; Vol. 17 No. 2 (2019): REVSTAT-Statistical Journal; 245-264pt-PTREVSTAT; Vol. 17 N.º 2 (2019): REVSTAT-Statistical Journal; 245-2642183-03711645-6726engjournal articlehttp://purl.org/coar/resource_type/c_6501literatureVoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85 |
| spellingShingle | Joint Modelling of Longitudinal and Competing Risks Data in Clinical Research Teixeira , Laetitia competing risks joint modelling longitudinal data peritoneal dialysis time-to-event data |
| status | SINGLETON |
| status_str | VoR |
| subject.fl_str_mv | competing risks joint modelling longitudinal data peritoneal dialysis time-to-event data |
| title | Joint Modelling of Longitudinal and Competing Risks Data in Clinical Research |
| title_full | Joint Modelling of Longitudinal and Competing Risks Data in Clinical Research |
| title_fullStr | Joint Modelling of Longitudinal and Competing Risks Data in Clinical Research |
| title_full_unstemmed | Joint Modelling of Longitudinal and Competing Risks Data in Clinical Research |
| title_short | Joint Modelling of Longitudinal and Competing Risks Data in Clinical Research |
| title_sort | Joint Modelling of Longitudinal and Competing Risks Data in Clinical Research |
| topic | competing risks joint modelling longitudinal data peritoneal dialysis time-to-event data |
| topic_facet | competing risks joint modelling longitudinal data peritoneal dialysis time-to-event data |
| url | https://doi.org/10.57805/revstat.v17i2.267 |
| visible | 1 |