The analysis of correlations within pairs of failure times is of interest to many research topics in medicine, such as the correlation of survival-type endpoints of twins, the correlation of times till failure in paired organs, or the correlation of survival time with a surrogate endpoint. The dependence of such times is assumed monotonic, and thus quantification by nonparametric correlation coefficients is appropriate. The typical censoring of such times requires more involved methods of statistical analysis. The SAS macro SURVCORR and the R package SurvCorr produce point and interval estimates of Spearman's r_S under possible censoring of both failure times, assuming a normal copula for the bivariate failure distribution and by implementing a novel iterative multiple imputation algorithm. This method provides an interesting alternative to implementations of maximum likelihood methodology for the normal copula approach, reducing computing time to about 0.2% without sacrificing statistical performance. For iterative multiple imputation, survival probabilities at death or censoring times are first transformed to normal deviates. Then, those deviates which relate to censored times are iteratively augmented, by conditional multiple imputation, until convergence is obtained for Spearman's r_S. The algorithm is presented in detail by Schemper et al (2013).

References:
Schemper,M., Kaider,A., Wakounig,S. & Heinze,G. (2013): "Estimating the correlation of bivariate failure times under censoring", Statistics in Medicine, 32, 4781-4790 (doi:10.1002/sim.5874).

The R package SurvCorr is available on CRAN. 

The current version of the SAS macro SURVCORR uses an executable program which assumes IMSL to be installed. We are working on an IMSL-independent version.

Our program is free of charge. However, before download, we would like you to supply your name and e-mail address here; we may then notify you if a new version is published: