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@article{1369394, author = {Fine, JP and Gray, RJ}, article_location = {Alexandria}, article_number = {446}, doi = {http://dx.doi.org/10.2307/2670170}, keywords = {hazard of subdistribution; martingale; partial likelihood; transformation model}, language = {eng}, issn = {0162-1459}, journal = {Journal of the American Statistical Association}, title = {A proportional hazards model for the subdistribution of a competing risk}, volume = {94}, year = {1999} }
TY - JOUR ID - 1369394 AU - Fine, JP - Gray, RJ PY - 1999 TI - A proportional hazards model for the subdistribution of a competing risk JF - Journal of the American Statistical Association VL - 94 IS - 446 SP - 496-509 EP - 496-509 PB - Amer Statistical Assoc SN - 01621459 KW - hazard of subdistribution KW - martingale KW - partial likelihood KW - transformation model N2 - With explanatory covariates, the standard analysis for competing risks data involves modeling the cause-specific hazard functions via a proportional hazards assumption. Unfortunately, the cause-specific hazard function does not have a direct interpretation in terms of survival probabilities for the particular failure type. In recent years many clinicians have begun using the cumulative incidence function, the marginal failure probabilities for a particular cause, which is intuitively appealing and more easily explained to the nonstatistician. The cumulative incidence is especially relevant in cost-effectiveness analyses in which the survival probabilities are needed to determine treatment utility. Previously, authors have considered methods for combining estimates of the cause-specific hazard functions under the proportional hazards formulation. However, these methods do not allow the analyst to directly assess the effect of a covariate on the marginal probability function. In this article we propose a novel semiparametric proportional hazards model for the subdistribution. Using the partial likelihood principle and weighting techniques, we derive estimation and inference procedures for the finite-dimensional regression parameter under a variety of censoring scenarios. We give a uniformly consistent estimator for the predicted cumulative incidence for an individual with certain covariates; confidence intervals and bands can be obtained analytically or with an easy-to-implement simulation technique. To contrast the two approaches, we analyze a dataset from a breast cancer clinical trial under both models. ER -
FINE, JP a RJ GRAY. A proportional hazards model for the subdistribution of a competing risk. \textit{Journal of the American Statistical Association}. Alexandria: Amer Statistical Assoc, 1999, roč.~94, č.~446, s.~496-509. ISSN~0162-1459. Dostupné z: https://dx.doi.org/10.2307/2670170.
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