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Effect of Berkson measurement error on parameter estimates in Cox regression models
Helmut Küchenhoff1, Ralf Bender, Ingo Langner
1Department of Statistics, Ludwig-Maximilians-Universität, Akademiestr. 1, 80799, München, Germany. kuechenhoff@stat.uni-muenchen.de
Lifetime Data Analysis
|April 3, 2007
Summary
Berkson measurement error has minimal impact on Cox proportional hazard models, especially with rare diseases. Even high variance errors cause negligible bias, though multiplicative errors show more pronounced effects.
Area of Science:
- Biostatistics
- Epidemiology
- Survival Analysis
Background:
- Measurement error can bias results in survival analysis.
- Berkson measurement error is a specific type of error that needs careful consideration.
- Cox proportional hazard models are widely used for survival data analysis.
Purpose of the Study:
- To investigate the impact of additive and multiplicative Berkson measurement error on Cox proportional hazard models.
- To assess the bias introduced by measurement error in survival data analysis.
Main Methods:
- Graphical methods were used to visualize the effect of measurement error on survival and hazard functions.
- Simulation studies were conducted to quantify the bias in parameter estimation.
- Analysis was performed using data from the German Uranium Miners Cohort Study.
Main Results:
- Small measurement errors and rare diseases showed no substantial bias in the Cox model.
- High variance Berkson measurement error resulted in negligible attenuation of the observed effect.
- Multiplicative measurement error exhibited a more pronounced attenuation effect compared to additive error.
Conclusions:
- Berkson measurement error, even with high variance, has a limited impact on Cox proportional hazard models, particularly in rare disease scenarios.
- The type of Berkson error (additive vs. multiplicative) influences the degree of bias observed.
- Careful consideration of measurement error is important for accurate survival data analysis.
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