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Bias analysis and the simulation-extrapolation method for survival data with covariate measurement error under
1Department of Statistics and Actuarial Science, University of Waterloo, Ontario, Canada.
Biometrical Journal. Biometrische Zeitschrift
|June 12, 2012
Summary
Ignoring measurement error in survival data can bias results. This study introduces the simulation-extrapolation method to correct bias in proportional odds models, offering a practical solution without covariate process modeling.
Area of Science:
- Statistics
- Biostatistics
- Survival Analysis
Background:
- Measurement error in covariates can lead to biased estimates in regression models.
- Existing research primarily addresses measurement error in proportional hazards (PH), accelerated failure time (AFT), and additive hazards (AH) models.
- The impact of measurement error on proportional odds (PO) models remains under-explored despite their importance.
Purpose of the Study:
- To investigate the bias induced by ignoring covariate measurement error in proportional odds models.
- To introduce and evaluate the simulation-extrapolation (SIMEX) method for adjusting this bias.
- To assess the performance and robustness of the proposed method.
Main Methods:
- The study employs the simulation-extrapolation (SIMEX) method to adjust for measurement error.
- Theoretical analysis establishes the asymptotic normality of the resulting estimators.
- Empirical evaluation includes simulation studies and an application to the Busselton Health Study data.
Main Results:
- Ignoring measurement error in covariates can substantially bias estimates in proportional odds models.
- The simulation-extrapolation method effectively adjusts for this bias.
- The proposed method is robust to potential misspecification of the error model.
Conclusions:
- The simulation-extrapolation method provides a straightforward and effective approach to handle measurement error in proportional odds models.
- This method avoids the need for complex covariate process modeling, reducing the risk of misspecification.
- The findings highlight the importance of accounting for measurement error in survival data analysis using proportional odds models.
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