Evaluation of Failure Time Surrogate Endpoints in Individual Patient Data Meta-Analyses

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) 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, 2019 ).


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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.

News

Changes in version 1.1.23 (2017-07-13)

  • new function simData.gh() to generate data from Gumbel-Hougaard copula

Changes in version 1.1.15 (2017-04-25)

  • fixed minor issue in prediction function for adjusted copula models

Changes in version 1.1.14 (2017-04-06)

  • fixed issue in prediction intervals for mixed Poisson models

Changes in version 1.1.13 (2017-03-28)

  • fixed issue in prediction intervals for adjusted copula models

Changes in version 1.1.12 (2017-03-02)

  • loocv() now also returns the values of kTau and R2 estimated in each (N-1) fold

Changes in version 1.1.10 (2017-02-27)

  • fixed issues with loocv when few trials (added controls)
  • added data 'gastadj'
  • added twoStage parameter to surrosurv for Shih and Louis (1995) approach to copula estimation

Changes in version 1.1.4 (2016-12-06)

  • added paper manuscript as vignette('surrosurv')

Changes in version 1.1 (2016-11-09)

  • Poisson models can be fitted each separately

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Changes in version 0.1.1 (2016-09-28)

  • fixed examples for poissonize()

Changes in version 0.1.0 (First Complete version, 2016-09-27)

  • new function loocv() (with print() and plot() functions) to compute leave-one-out cross-validation

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Changes in version 0.0.15 (2016-09-23)

  • new function ste() to compute the surrogate threshold effect
  • plot.sussosurv() can now show prediction intervals and the STE

Changes in version 0.0.11 (2016-08-12)

  • predict and plot for class sussosurv

Changes in version 0.0.10 (2016-08-01)

  • kkt2 convergence criteria corrected from positive determinant to positive min eigenvalue

Changes in version 0.0.9 (2016-07-28)

  • bugfix in Poisson method, which did not return results because of mispelled model name

Changes in version 0.0.7 (2016-07-25)

  • the Kendall's tau for copulas is now initialized using the SurvCorr package (much faster)

Changes in version 0.0.6 (2016-07-22)

  • added the function convals(), giving the values of the max abs gradient and the min eigenvalue of the variance-covariance matrix of the random treatment effects
  • the function convergence() uses explicit computation provided by covals(), instead of using the function optimx in the package optimx

Reference manual

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install.packages("surrosurv")

1.1.25 by Stefan Michiels, 3 months ago


Browse source code at https://github.com/cran/surrosurv


Authors: Federico Rotolo [aut] , Xavier Paoletti [ctr] , Marc Buyse [ctr] , Tomasz Burzykowski [ctr] , Stefan Michiels [ctr, cre]


Documentation:   PDF Manual  


Task views:


GPL-2 license


Imports copula, eha, lme4, MASS, Matrix, msm, mvmeta, optextras, parallel, parfm, survival, SurvCorr

Depends on stats, optimx, grDevices

Suggests R.rsp


See at CRAN