Name
Title | ||
|---|---|---|
Joint modeling of longitudinal and time-to-event data on multivariate protein biomarkers. |
Abstract | ||
|---|---|---|
The methodological advancements in multivariate joint modeling are not substantially utilized in the field of omics analysis. The objective of this study is to provide a brief theoretical background on the modeling and explain the use of this method in real proteomics data. The study uses multivariate joint modeling of longitudinal and time to event data to establish the relationship between longitudinal biomarker measurements and the duration to relapse. Also, it elucidates the use of multivariate joint model fitting and validation along with the applicability of this method on capturing and predicting the disease-free survival duration in the presence of multiple longitudinal biomarkers. The study recommends the use of a multivariate joint model fit to obtain a broader view of the underlying association between multiple biomarkers and relapse duration. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1016/j.cam.2020.113016 | Journal of Computational and Applied Mathematics |
Keywords | DocType | Volume |
Survival modeling,Longitudinal data,Protein expression analysis,JMbayes | Journal | 381 |
ISSN | Citations | PageRank |
0377-0427 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Abin Thomas | 1 | 0 | 0.34 |
| Gajendra K. Vishwakarma | 2 | 3 | 5.12 |
| Atanu Bhattacharjee | 3 | 0 | 0.68 |