Search

Suppose HT ht . One may try to remove such ambiguity by restricting the minimum support of HT ht to be greater than I m i , but this essentially excludes the possibility that the primary event censors the mediating event. Therefore, the usual notion of having well-defined interventions for I A i and I M i for defining HT ht does not go through because HT ht may happen before I m i , and in that case, the role of I m i is ambiguous. Under a composition assumption, HT ht . [Extracted from the article]

There has been a recent emphasis on the identification of biomarkers and other biologic measures that may be potentially used as surrogate endpoints in clinical trials. We focus on the setting of data from a single clinical trial. In this article, we consider a framework in which the surrogate must occur before the true endpoint. This suggests viewing the surrogate and true endpoints as semicompeting risks data; this approach is new to the literature on surrogate endpoints and leads to an asymmetrical treatment of the surrogate and true endpoints. However, such a data structure also conceptually complicates many of the previously considered measures of surrogacy in the literature. We propose novel estimation and inferential procedures for the relative effect and adjusted association quantities proposed by Buyse and Molenberghs (1998, Biometrics 54, 1014–1029). The proposed methodology is illustrated with application to simulated data, as well as to data from a leukemia study. [ABSTRACT FROM AUTHOR]

The product-limit estimator calculated from data subject to random left-truncation relies on the testable assumption of quasi-independence between the failure time and the truncation time. In this paper, we propose a model for a truncated sample of pairs (Xi,Yi) satisfying Yi > Xi. A possible dependency between the truncation time and the variable of interest is modelled with a parametric family of copulas. The model also features a distribution function FX(.) and a survival distribution SY(.) associated with the marginal behaviours of X and Y in the observable region Y > X. Semiparametric estimators for these two functions are proposed; they do not make any parametric assumption about either FX(.) or SY(.). We derive an estimator for the copula parameter α based on the conditional Kendall's tau. We generalise the copula-graphic estimators of Zheng & Klein (1995) to truncated variables. The asymptotic distributions of all these estimators are then investigated. The methods are illustrated with a real dataset on HIV infection by transfusion and by simulations. [ABSTRACT FROM AUTHOR]

SUMMARY Using a model in which treatment affects only the failure type of primary clinical interest, the use of a cause-specific analysis is examined when the latent failure times are derived from a frailty model. The relationship between the cause-specific hazard ratio and the marginal hazard ratio is expressed in terms of a local dependence measure of the latent failure times. [ABSTRACT FROM AUTHOR]

Frailty and copula models specify a parametric dependence structure for multivariate failure-time data. Estimation of some joint quantities can be highly sensitive to the assumed parametric form, and hence model fit is an important issue. This paper lays out a general diagnostic framework for evaluating and selecting frailty and copula models. The approach is based on the cumulative sum of residuals that are calculated in bivariate time. The residuals reflect the difference between the observed and expected bivariate association structures. The proposed model-checking process is interpretable with a limiting distribution which can be approximated using the bootstrap. Simulations and a data example illustrate the practical application of the method. [ABSTRACT FROM AUTHOR]