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>.

Estimates 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, et al (2020) "Assessing the Value of a Censored Surrogate Outcome" <doi:10.1007/s10985-019-09473-1> and Wang et al (2025) "Model-free Approach to Evaluate a Censored Intermediate Outcome as a Surrogate for Overall Survival" <doi:10.1002/sim.70268>.  A tutorial for this package can be found at <https://www.laylaparast.com/surrogateoutcome>.

Provides functions to nonparametrically assess assumptions necessary to prevent the surrogate paradox through hypothesis tests of stochastic dominance, monotonicity of regression functions, and non-negative residual treatment effects. More details are available in Hsiao et al 2025 (under review). A tutorial for this package can be found at <https://laylaparast.com/home/SurrogateParadoxTest.html>.