Abstract
In longitudinal studies, clinicians usually collect longitudinal biomarkers’ measurements over time until an event such as recovery, disease relapse, or death occurs. Joint modeling approaches are increasingly used to study the association between one longitudinal and one survival outcome. However, in practice, a patient may experience multiple disease progression events successively. So instead of modeling of a single event, progression of the disease as a multistate process should be modeled. On the other hand, in such studies, multivariate longitudinal outcomes may be collected and their association with the survival process is of interest. In the present study, we applied a joint model of various longitudinal biomarkers and transitions between different health statuses in patients who underwent renal transplantation. The full joint likelihood approaches are faced with the complexities in computation of the likelihood. So, here, we have proposed two-stage modeling of multivariate longitudinal outcomes and multistate conditions to avoid these complexities. The proposed model showed reliable results compared to the joint model in case of joint modeling of univariate longitudinal biomarker and the multistate process.
Highlights
In longitudinal medical studies, repeated measurements of biomarkers are usually collected until the occurrence of a clinical event such as recovery, disease relapse, or death. e main objective of these studies is to study the association between two correlated processes or to use the information of biomarkers to predict or explain the time-to-event
The results showed that Donor-Recipient Gender (DRG) was associated with the creatinine trajectory and age of recipient was associated with hemoglobin, and blood urea nitrogen (BUN) measurements (Table 1)
In longitudinal health studies, a patient may experience a succession of clinical events instead of a single event and multiple longitudinal biomarkers may be measured over time
Summary
In longitudinal medical studies, repeated measurements of biomarkers are usually collected until the occurrence of a clinical event such as recovery, disease relapse, or death. e main objective of these studies is to study the association between two correlated processes or to use the information of biomarkers to predict or explain the time-to-event. Instead of the occurrence of a single event, the progression of the disease should be modeled as a multistate process, focusing on the transitions between different health states and the impact of the longitudinal biomarker on them. In such studies, multiple longitudinal biomarkers may be Journal of Probability and Statistics collected for each patient and the correlation structure between them should be taken into account in the model [4]. Here, we use the idea of these initial approaches to avoid the computational complexities and propose a two-stage base model for joint modeling of multivariate longitudinal and multistate data.
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