surrosurv: An R package for the evaluation of failure time surrogate endpoints in individual patient data meta-analyses of randomized clinical trials.BACKGROUND AND OBJECTIVE
 The R package surrosurv implements the classical two-step method with Clayton, Plackett, and Hougaard copulas.  The surrosurv package provides an R implementation of classical and recent statistical methods for surrogacy assessment of failure time endpoints. 
surrosurv
surrosurv: An R package for the evaluation of failure time surrogate endpoints in individual patient data meta-analyses of randomized clinical trials.BACKGROUND AND OBJECTIVE
 The R package surrosurv implements the classical two-step method with Clayton, Plackett, and Hougaard copulas. 
R package
surrosurv: An R package for the evaluation of failure time surrogate endpoints in individual patient data meta-analyses of randomized clinical trials.BACKGROUND AND OBJECTIVE
 The R package surrosurv implements the classical two-step method with Clayton, Plackett, and Hougaard copulas.  The surrosurv package provides an R implementation of classical and recent statistical methods for surrogacy assessment of failure time endpoints. 
surrosurv
We aimed at providing an R implementation of classical and well-established as well as more recent statistical methods for surrogacy assessment with failure time endpoints.  The surrosurv package provides an R implementation of classical and recent statistical methods for surrogacy assessment of failure time endpoints. 
R implementation
First, a Kendall's τ is estimated as measure of individual level surrogacy using a copula model.  The most common data simulation models described in the literature are based on: copula models, mixed proportional hazard models, and mixture of half-normal and exponential random variables.



copula model
The mixed Poisson approach is implemented with different reduced models in addition to the full model. 
Poisson approach
The most common data simulation models described in the literature are based on: copula models, mixed proportional hazard models, and mixture of half-normal and exponential random variables.



mixed proportional hazard models
The most common data simulation models described in the literature are based on: copula models, mixed proportional hazard models, and mixture of half-normal and exponential random variables.



mixture of half-normal and exponential random variables