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surrosurv  

Evaluation of Failure Time Surrogate Endpoints in Individual Patient Data Meta-Analyses
View on CRAN: Click here


Download and install surrosurv package within the R console
Install from CRAN:
install.packages("surrosurv")

Install from Github:
library("remotes")
install_github("cran/surrosurv")

Install by package version:
library("remotes")
install_version("surrosurv", "1.1.27")



Attach the package and use:
library("surrosurv")
Maintained by
Dan Chaltiel
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2016-11-10
Latest Update: 2025-09-03
Description:
Provides functions for the evaluation of surrogate endpoints when both the surrogate and the true endpoint are failure time variables. The approaches implemented are: (1) the two-step approach (Burzykowski et al, 2001) <doi:10.1111/1467-9876.00244> with a copula model (Clayton, Plackett, Hougaard) at the first step and either a linear regression of log-hazard ratios at the second step (either adjusted or not for measurement error); (2) mixed proportional hazard models estimated via mixed Poisson GLM (Rotolo et al, 2017 <doi:10.1177/0962280217718582>).
How to cite:
Dan Chaltiel (2016). surrosurv: Evaluation of Failure Time Surrogate Endpoints in Individual Patient Data Meta-Analyses. R package version 1.1.27, https://cran.r-project.org/web/packages/surrosurv. Accessed 13 Oct. 2026.
Previous versions and publish date:
(2026-07-09 07:10), 1.0.0 (2016-11-10 08:19), 1.1.5 (2016-12-09 02:08), 1.1.10 (2017-02-28 00:34), 1.1.15 (2017-05-03 16:50), 1.1.24 (2017-09-27 10:26), 1.1.25 (2019-01-18 22:50), 1.1.26 (2023-04-14 10:50)
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Other R packages that surrosurv depends, imports, suggests or enhances
Complete documentation for surrosurv
Functions, R codes and Examples using the surrosurv R package
Some associated functions: convergence . gastadj . gastadv . loocv . poissonize . simData . ste . surrosurv-package . surrosurv . 
Some associated R codes: convergence.R . copulas_functions.R . fr.lognormal.R . loocv.R . poisson_functions.R . poissonize.R . predict.R . simData.R . surrosurv.R . survcorr.R .  Full surrosurv package functions and examples
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