DeepAJM: Deep Association Joint Model for Irregularly Sampled data
Joint Models simultaneously model longitudinal and survival outcomes, leveraging patterns in patients' longitudinal trajectory to improve the prediction of survival outcomes. The classical parametric joint models, however, rely on fixed parametric assumptions, making them susceptible to bias under model misspecification and smaller sample sizes. We propose a deep joint model, DeepAJM, that does not require any parametric assumptions, while retaining a partially interpretable, per-longitudinal-outcome association structure. The joint model uses an encoder-decoder (sequence-to-sequence) architecture to learn the latent structure in patients' time-varying covariate trajectories. The model li...
| Authors | Barsha Halder, Jeffrey A. Thompson |
|---|---|
| Primary category | stat.AP |
| Published | 5 October 2026 |
| Last updated at source | 7 October 2026 |
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