Semiparametric regression analysis for clustered failure time data

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Abstract
Inference procedures based on the partial likelihood function for the Cox proportional hazards model have been generalised to the case in which the data consist of a large number of independent small groups of correlated failure time observations (Lee, Wei [amp ] Amato, 1992; Liang, Self [amp ] Chang, 1993; Cai [amp ] Prentice, 1997). However, the Cox model may not fit the data well. A class of linear transformation models, which includes the proportional hazards and odds models as special cases, has been studied extensively for univariate event times. In this paper, statistical methods to analyse such correlated observations are proposed for these models. We use the data from a recent study of the genetic aetiology of alcoholism to illustrate the new procedures for estimation, prediction and model selection.

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