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 
  (Grant Agreement no 602552), the Special Research Fund (BOF) of Hasselt 
  University (BOF-number: BOF2OCPO3), GlaxoSmithKline Biologicals, Baekeland 
  Mandaat (HBC.2022.0145), and Johnson & Johnson Innovative Medicine.

Provides functions to estimate the proportion of treatment effect explained by the surrogate marker using a Bayesian Model Averaging approach. Duan and Parast (2023) <doi:10.1002/sim.9986>.

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 Parast, Tian, Cai (2020) "Assessing the Value of a Censored Surrogate Outcome" <doi:10.1007/s10985-019-09473-1>. 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.