Clayton's model for association in bivariate survival data is both of intrinsic importance and an interesting example of a semiparametric estimation problem, that is, a problem where inference about a parameter is required in the presence of nuisance functions. The joint distribution of the two survival times in this model is absolutely continuous and a single parameter governs the association between the two survival times. In this paper we describe an algorithm to derive the asymptotic lower bound for the information of the parameter governing the association. We discuss the construction of one-step estimators and compare their performance to that of other estimators in a Monte Carlo study.
"Semiparametric Estimation of Association in a Bivariate Survival Function." Ann. Statist. 21 (3) 1648 - 1662, September, 1993. https://doi.org/10.1214/aos/1176349277