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, 2019 <DOI:10.1177/0962280217718582>).
The DESCRIPTION file:
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Federico Rotolo [aut], Xavier Paoletti [ctr], Marc Buyse [ctr], Tomasz Burzykowski [ctr], Stefan Michiels [ctr, cre]
Maintainer: Stefan Michiels <firstname.lastname@example.org>
Rotolo F, Paoletti X, Michiels S. surrosurv: an R Package for the Evaluation of Failure Time Surrogate Endpoints in Individual Patient Data Meta-Analyses of Randomized Clinical Trials. Computer Methhods and Programs in Biomedicine 2018; doi: 10.1016/j.cmpb.2017.12.005
Rotolo F, Paoletti X, Burzykowski T, Buyse M, Michiels S. A Poisson approach for the validation of failure time surrogate endpoints in individual patient data meta-analyses. Statistical Methhods in Medical Research 2019; 28(1). doi: 10.1177/0962280217718582
Burzykowski T, Molenberghs G, Buyse M et al. Validation of surrogate end points in multiple randomized clinical trials with failure time end points. Journal of the Royal Statistical Society C 2001; 50:405–422. doi: 10.1111/1467-9876.00244
Gasparrini A, Armstrong B, Kenward MG. Multivariate meta-analysis for non-linear and other multi-parameter associations. Statistics in Medicine 2012; 31:3821–39. doi: 10.1002/sim.5471
Burzykowski T, Molenberghs G, Buyse M (2005). The Evaluation of Surrogate Endpoints. Springer, New York. http://rd.springer.com/book/10.1007/b138566
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