Joint Models for Longitudinal and Competing Risks Data
Hong Wang [aut, cre], Ning Li [ctb],Shanpeng Li[ctb] and Gang Li [ctb]
Fit joint models of continuous or ordinal longitudinal data and time-to-event data with competing risks. For a detailed information, see Robert Elashoff, Gang Li and Ning Li (2016, ISBN:9781439807828); Robert M. Elashoff,Gang Li and Ning Li (2008) <doi:10.1111/j.1541-0420.2007.00952.x> ; Ning Li, Robert Elashoff, Gang Li and Jeffrey Saver (2010) <doi:10.1002/sim.3798> .

Hong Wang [aut, cre], Ning Li [ctb] and Gang Li [ctb]

Parameter models for the joint modeling of longitudinal (continuous or ordinal) data and time-to-event data with competing risks. For a detailed information, see Robert Elashoff, Gang li and Ning Li (2016, ISBN:9781439807828) ; Robert M. Elashoff,Gang Li and Ning Li (2008) <doi:10.1111/j.1541-0420.2007.00952.x> ; Ning Li, Robert Elashoff, Gang Li and Jeffrey Saver (2010) <doi:10.1002/sim.3798> .

Emil A. Cornea, Liddy M. Chen, Bahjat F. Qaqish, Haitao Chu, and Joseph G. Ibrahim
Performs power calculations for joint modeling of longitudinal and survival data with k-th order trajectories when the variance-covariance matrix Sigma_theta is unknown.

Performs power calculations for joint modeling of longitudinal and survival data with k-th order trajectories when the variance-covariance matrix, Sigma_theta, is unknown.

Emil A. Cornea, Liddy M. Chen, Bahjat F. Qaqish, Haitao Chu, and
Joseph G. Ibrahim
Performs power calculations for joint modeling of longitudinal
and survival data with k-th order trajectories when the variance-covariance
matrix, Sigma_theta, is unknown.

JOP is a tool for simultanous optimization of multiple
responses and visualization of the results. The visualization
is done by the joint optimization plot introduced by Kuhnt and
Erbruegge (2004).

JOP is a tool for simultaneous optimization of multiple
responses and visualization of the results. The visualization
is done by the joint optimization plot introduced by Kuhnt and
Erbruegge (2004).