Joint Modelling of Longitudinal and Competing Risks Data in Clinical Research
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 an...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Instituto Nacional de Estatística | Statistics Portugal
2019-04-01
|
| Series: | Revstat Statistical Journal |
| Subjects: | |
| Online Access: | https://revstat.ine.pt/index.php/REVSTAT/article/view/267 |
| _version_ | 1818513306222067712 |
|---|---|
| author | Laetitia Teixeira Inês Sousa Anabela Rodrigues Denisa Mendonça |
| author_facet | Laetitia Teixeira Inês Sousa Anabela Rodrigues Denisa Mendonça |
| author_sort | Laetitia Teixeira |
| collection | DOAJ |
| 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.
|
| first_indexed | 2024-12-10T23:59:26Z |
| format | Article |
| id | doaj.art-a87b1d2287f64c90a9645fed976430a2 |
| institution | Directory Open Access Journal |
| issn | 1645-6726 2183-0371 |
| language | English |
| last_indexed | 2024-12-10T23:59:26Z |
| publishDate | 2019-04-01 |
| publisher | Instituto Nacional de Estatística | Statistics Portugal |
| record_format | Article |
| series | Revstat Statistical Journal |
| spelling | doaj.art-a87b1d2287f64c90a9645fed976430a22022-12-22T01:28:31ZengInstituto Nacional de Estatística | Statistics PortugalRevstat Statistical Journal1645-67262183-03712019-04-0117210.57805/revstat.v17i2.267Joint Modelling of Longitudinal and Competing Risks Data in Clinical ResearchLaetitia Teixeira 0Inês Sousa 1Anabela Rodrigues 2Denisa Mendonça 3Universidade do PortoUniversidade do MinhoUniversidade do PortoUniversidade do Porto 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. https://revstat.ine.pt/index.php/REVSTAT/article/view/267competing risksjoint modellinglongitudinal dataperitoneal dialysistime-to-event data |
| spellingShingle | Laetitia Teixeira Inês Sousa Anabela Rodrigues Denisa Mendonça Joint Modelling of Longitudinal and Competing Risks Data in Clinical Research Revstat Statistical Journal 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 |
| url | https://revstat.ine.pt/index.php/REVSTAT/article/view/267 |
| work_keys_str_mv | AT laetitiateixeira jointmodellingoflongitudinalandcompetingrisksdatainclinicalresearch AT inessousa jointmodellingoflongitudinalandcompetingrisksdatainclinicalresearch AT anabelarodrigues jointmodellingoflongitudinalandcompetingrisksdatainclinicalresearch AT denisamendonca jointmodellingoflongitudinalandcompetingrisksdatainclinicalresearch |