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Sensitivity analysis for informative censoring in parametric survival models

Sensitivity analysis for informative censoring in parametric survival models Abstract Most statistical methods for censored survival data assume there is no dependence between the lifetime and censoring mechanisms, an assumption which is often doubtful in practice. In this paper we study a parametric model which allows for dependence in terms of a parameter δ and a bias function B ( t , θ). We propose a sensitivity analysis on the estimate of the parameter of interest for small values of δ. This parameter measures the dependence between the lifetime and the censoring mechanisms. Its size can be interpreted in terms of a correlation coefficient between the two mechanisms. A medical example suggests that even a small degree of dependence between the failure and censoring processes can have a noticeable effect on the analysis. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Biostatistics Oxford University Press

Sensitivity analysis for informative censoring in parametric survival models

Abstract

Abstract Most statistical methods for censored survival data assume there is no dependence between the lifetime and censoring mechanisms, an assumption which is often doubtful in practice. In this paper we study a parametric model which allows for dependence in terms of a parameter δ and a bias function B ( t , θ). We propose a sensitivity analysis on the estimate of the parameter of interest for small values of δ. This parameter measures the dependence between the lifetime and the censoring mechanisms. Its size can be interpreted in terms of a correlation coefficient between the two mechanisms. A medical example suggests that even a small degree of dependence between the failure and censoring processes can have a noticeable effect on the analysis.
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