Joint model for repeated measurements and competing risks data using flexible shared random effects
Avinash Kumar & M. S. Panwar
What the paper says
In advanced clinical trials, clinicians collect data on both time-to-event outcomes with respective causes and longitudinal characteristics during each follow-up visit. Such experiments motivate researchers to develop feasible models suitable for the inference of the obtained data. In this article, a joint model is designed for longitudinal data or repeated measurement data and time-to-event data generated in the presence of competing risks. For the longitudinal data, a linear mixed-effects model is considered to capture the fixed effects of covariates on longitudinal measurements, while the random effects account for between-individual variation over the visiting time points. For the competing risks process, a generalized exponential distribution is used, with the scale parameter modeled as an exponential function of a linear combination of covariates. To link these two processes, a shared random effects association structure is employed. The parameters of the joint model are estimated using the maximum likelihood technique via the Expectation-Maximization algorithm, where the random effects are treated as latent variables. Additionally, a simulation study is conducted to evaluate the performance of the joint model. Finally, the model is applied to real-life data from the SANAD trial, demonstrating its practical utility.
Evidence weight
Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40
| F · citation impact | 0.50 × 0.4 = 0.20 |
| M · momentum | 0.50 × 0.15 = 0.07 |
| V · venue signal | 0.50 × 0.05 = 0.03 |
| R · text relevance † | 0.50 × 0.4 = 0.20 |
† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.