A simulation-extrapolation approach for the mixture cure model with mismeasured covariates

TL;DR

This paper introduces a simulation-extrapolation method to correct bias in mixture cure models with mismeasured covariates, enabling more accurate survival analysis in medical studies.

Contribution

It develops a bias correction approach using simex for mixture cure models with mismeasured covariates, combining logistic and Cox models for practical survival analysis.

Findings

Estimators are consistent and asymptotically normal.

Simulation study confirms finite sample performance.

Applied to prostate cancer data for PSA effect analysis.

Abstract

We consider survival data from a population with cured subjects in the presence of mismeasured covariates. We use the mixture cure model to account for the individuals that will never experience the event and at the same time distinguish between the effect of the covariates on the cure probabilities and on survival times. In particular, for practical applications, it seems of interest to assume a logistic form of the incidence and a Cox proportional hazards model for the latency. To correct the estimators for the bias introduced by the measurement error, we use the simex algorithm, which is a very general simulation based method. It essentially estimates this bias by introducing additional error to the data and then recovers bias corrected estimators through an extrapolation approach. The estimators are shown to be consistent and asymptotically normally distributed when the true extrapolation function is known. We investigate their finite sample performance through a simulation study and apply the proposed method to analyse the effect of the prostate specific antigen (PSA) on patients with prostate cancer.