Publications and Preprints

Estimation of cumulative incidence functions in competing risks studies under an order restriction
Hammou El Barmi, S. C. Kochar, Hari Mukerjee and Francisco J. Samaniego
In the competing risks problem an important role is played by the cumulative incidence function (CIF), whose value at time t is the probability of failure by time $t$ for a particular type of failure in the presence of other risks. Its estimation and asymptotic distribution theory have been studied by many. In some cases there are reasons to believe that the CIF’s due to two types of failure are order restricted. Several procedures have appeared in the literature for testing for such orders. In this paper we initiate the study of estimation of two CIF’s subject to a type of stochastic ordering, both when there are just two causes of failure and when there are more than two causes of failure, treating those other than the two of interest as a censoring mechanism. We do not assume independence of the two types of failure of interest, however, these are assumed to be independent of the other causes in the censored case. Weak convergence results for the estimators have been derived. It is shown that when the order restriction is strict, the asymptotic distributions are the same as those for the empirical estimators without the order restriction. Thus we get the restricted estimators “free of charge”, at least in the asymptotic sense. When the two CIF’s are equal, the asymptotic MSE is reduced by using the order restriction. For finite sample sizes simulations seem to indicate that the restricted estimators have uniformly smaller MSE’s than the unrestricted ones in all cases.

isid/ms/2002/19 [fulltext]

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