rgl, lattice, latticeExtra, survival, nlme, lme4,
OrdinalLogisticBiplot, logistf, rms, mixtools, parallel

Wim Van der Elst, Paul Meyvisch, Alvaro Florez Poveda, Ariel Alonso, Hannah M. Ensor, Christopher J. Weir & Geert Molenberghs
rgl, lattice, latticeExtra, survival, nlme, lme4,
OrdinalLogisticBiplot, logistf, rms, mixtools, parallel, ks,
rootSolve, extraDistr

MASS, rgl, lattice, latticeExtra, survival, nlme, lme4, msm,
OrdinalLogisticBiplot, logistf, rms, mixtools, parallel, ks,
rootSolve, extraDistr

MASS, lattice, latticeExtra, survival, nlme, lme4, msm,
OrdinalLogisticBiplot, logistf, rms, mixtools, parallel, ks,
rootSolve, extraDistr

MASS, lattice, latticeExtra, survival, nlme, lme4, msm,
OrdinalLogisticBiplot, logistf, rms, mixtools, parallel, ks,
rootSolve, extraDistr, pbapply

Wim Van der Elst, Fenny Ong, Paul Meyvisch, Alvaro Florez Poveda, Ariel Alonso, Hannah M. Ensor, Christopher J. Weir & Geert Molenberghs

MASS, lattice, latticeExtra, survival, nlme, lme4, msm,
logistf, rms, mixtools, parallel, ks, rootSolve, extraDistr,
pbapply

Wim Van der Elst, Florian Stijven, Fenny Ong, Paul Meyvisch, Alvaro Florez Poveda, Ariel Alonso, Hannah M. Ensor, Christopher J. Weir & Geert Molenberghs
MASS, lattice, latticeExtra, survival, nlme, lme4, msm,
logistf, rms, parallel, ks, extraDistr, pbapply, copula,
flexsurv, kdecopula, mvtnorm, rvinecopulib, dplyr

Estimation of the Proportion of Treatment Effect Explained by
Surrogate Outcome Information
Provides functions to estimate the proportion of treatment effect on a censored primary outcome that is explained by the treatment effect on a censored surrogate outcome/event. All methods are described in detail in "Assessing the Value of a Censored Surrogate Outcome" by Parast L, Tian L, and Cai T which is currently in press at Lifetime Data Analysis. The main functions are (1) R.q.event() which calculates the proportion of the treatment effect (the difference in restricted mean survival time at time t) explained by surrogate outcome information observed up to a selected landmark time, (2) R.t.estimate() which calculates the proportion of the treatment effect explained by primary outcome information only observed up to a selected landmark time, and (3) IV.event() which calculates the incremental value of the surrogate outcome information.