Fast and efficient joint modelling of multivariate longitudinal data and time-to-event data with a pairwise-fitting approach
Dries De Witte et al.
What the paper says
In empirical studies, multiple outcomes are often measured repeatedly over time, and interest frequently lies in studying the association between these longitudinal outcomes and a time-to-event outcome. Therefore, shared-parameter joint models for longitudinal and time-to-event outcomes have been developed. However, while such joint models in theory also allow for multiple longitudinal outcomes, they are often restricted to a limited number of outcomes due to computational complexity when fitting the models. To address this problem, we propose a new joint model, which is based on correlated instead of shared random effects, and for which a pairwise-modelling strategy can be used. In this approach, the longitudinal outcomes are modelled with (generalized) linear mixed models and the survival outcome with a Weibull proportional hazards frailty model. Instead of fitting the full joint model, this approach involves fitting all possible bivariate models, and inference is based on pseudo-likelihood theory. The main advantage of our approach is that there is no restriction on the number of longitudinally measured outcomes that are jointly modelled with the time-to-event outcome.
1 citation
Evidence weight
Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40
| F · citation impact | 0.16 × 0.4 = 0.06 |
| M · momentum | 0.53 × 0.15 = 0.08 |
| 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.