Inference in a survival cure model with mismeasured covariates using a simulation-extrapolation approach

Aurelie Bertrand, Catherine Legrand, Raymond J. Carroll, Christophe de Meester and Ingrid Van Keilegom ()

Biometrika, 2017, vol. 104, issue 1, 31-50

Abstract: SUMMARY In many situations in survival analysis, it may happen that a fraction of individuals will never experience the event of interest: they are considered to be cured. The promotion time cure model takes this into account. We consider the case where one or more explanatory variables in the model are subject to measurement error, which should be taken into account to avoid biased estimators. A general approach is the simulation-extrapolation algorithm, a method based on simulations which allows one to estimate the effect of measurement error on the bias of the estimators and to reduce this bias. We extend this approach to the promotion time cure model. We explain how the algorithm works, and we show that the proposed estimator is approximately consistent and asymptotically normally distributed, and that it performs well in finite samples. Finally, we analyse a database in cardiology: among the explanatory variables of interest is the ejection fraction, which is known to be measured with error.

Keywords: Bias correction; Cure fraction; Measurement error; Promotion time cure model; Semiparametric method. (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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