On semiparametric modelling, estimation and inference for survival data subject to dependent censoring

Identifiability of the multinormal and other distributions under competing risks model

N W Deresa and I Van Keilegom

Biometrika, 2021, vol. 108, issue 4, 965-979

Abstract: SummaryWhen modelling survival data, it is common to assume that the survival timeis conditionally independent of the censoring timegiven a set of covariates. However, there are numerous situations in which this assumption is not realistic. The goal of this paper is therefore to develop a semiparametric normal transformation model which assumes that, after a proper nonparametric monotone transformation, the vectorfollows a linear model, and the vector of errors in this bivariate linear model follows a standard bivariate normal distribution with a possibly nondiagonal covariance matrix. We show that this semiparametric model is identifiable, and propose estimators of the nonparametric transformation, the regression coefficients and the correlation between the error terms. It is shown that the estimators of the model parameters and the transformation are consistent and asymptotically normal. We also assess the finite-sample performance of the proposed method by comparing it with an estimation method under a fully parametric model. Finally, our method is illustrated using data from the AIDS Clinical Trial Group 175 study.

Keywords: Association; Dependent censoring; Nonparametric transformation; Survival analysis (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (4)

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