Description: In a clinical trial, it frequently occurs that the most credible outcome to evaluate the effectiveness of a new therapy (the true endpoint) is difficult to measure. In such a situation, it can be an effective strategy to replace the true endpoint by a (bio)marker that is easier to measure and that allows for a prediction of the treatment effect on the true endpoint (a surrogate endpoint). The package 'Surrogate' allows for an evaluation of the appropriateness of a candidate surrogate endpoint based on the meta-analytic, information-theoretic, and causal-inference frameworks. Part of this software has been developed using funding provided from the European Union's Seventh Framework Programme for research, technological development and demonstration under Grant Agreement no 602552.

OrdinalLogisticBiplot, logistf, rms, mixtools, parallel

Author: Wim Van der Elst, Paul Meyvisch, Alvaro Florez Poveda, Ariel Alonso, Hannah M. Ensor, Christopher J. Weir & Geert Molenberghs
OrdinalLogisticBiplot, logistf, rms, mixtools, parallel, ks,

Imports: MASS, rgl, lattice, latticeExtra, survival, nlme, lme4, msm,

Imports: MASS, lattice, latticeExtra, survival, nlme, lme4, msm,

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

logistf, rms, mixtools, parallel, ks, rootSolve, extraDistr,

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

Title: Estimation of the Proportion of Treatment Effect Explained by
Description: 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.