JLPM-package

<p>Functions for the estimation of joint latent process models (JLPM).
Continuous and ordinal outcomes are handled for the longitudinal part,
whereas the survival part considers multiple competing events.
The likelihood is computed using Monte-Carlo integration. Estimation is achieved
by maximizing the log-likelihood using a robust iterative algorithm.</p>

package

Estimation of extended joint models with shared random effects. Longitudinal data are handled in latent process models for continuous (Gaussian or curvilinear) and ordinal outcomes while proportional hazard models are used for the survival part. We propose a frequentist approach using maximum likelihood estimation. See Saulnier et al, 2022 <doi:10.1016/j.ymeth.2022.03.003>.

Viviane Philipps

JLPM

Joint Latent Process Models

Tiphaine Saulnier

Cecile Proust-Lima

JLPM-package function

<p>Cecile Proust-Lima, Viviane Philipps, Tiphaine Saulnier</p>

JLPM-package: Estimation of joint latent process models

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