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

Aurelie Bertrand1, Catherine Legrand1, Raymond J Carroll2

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Summary
This summary is machine-generated.

This study introduces a new method to correct biased results in survival analysis, particularly for cure models with measurement errors in explanatory variables. The simulation-extrapolation algorithm improves accuracy for predicting outcomes in situations with imperfect data.

Keywords:
Bias correctionCure fractionMeasurement errorPromotion time cure modelSemiparametric method

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Area of Science:

  • Biostatistics
  • Survival Analysis
  • Epidemiology

Background:

  • Survival analysis often encounters situations where a fraction of subjects are 'cured' and will never experience the event of interest.
  • Promotion time cure models account for this cure fraction.
  • Explanatory variables in these models can be subject to measurement error, leading to biased estimators.

Purpose of the Study:

  • To extend the simulation-extrapolation (SIMEX) algorithm to the promotion time cure model.
  • To address and reduce bias caused by measurement error in explanatory variables within cure models.
  • To provide a statistically sound method for analyzing data with both cure fractions and measurement error.

Main Methods:

  • The study adapts the simulation-extrapolation (SIMEX) algorithm for use with promotion time cure models.
  • The SIMEX approach involves simulations to estimate and correct for the bias introduced by measurement error.
  • Theoretical properties of the proposed estimator, including consistency and asymptotic normality, are investigated.

Main Results:

  • The proposed estimator, extended from the SIMEX algorithm, is shown to be approximately consistent and asymptotically normally distributed.
  • The method demonstrates good performance in finite sample simulations.
  • The analysis of a cardiology database, including the ejection fraction (a variable known to have measurement error), illustrates the practical application.

Conclusions:

  • The extended SIMEX algorithm effectively handles measurement error in explanatory variables within promotion time cure models.
  • The developed method provides reliable and less biased estimates in the presence of measurement error.
  • This approach offers a valuable tool for survival data analysis in fields like cardiology where measurement errors are common.