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Measurement error correction in the least absolute shrinkage and selection operator model when validation data are
Monica M Vasquez1,2, Chengcheng Hu1, Denise J Roe1
11 Mel and Enid Zuckerman College of Public Health, The University of Arizona, Tucson, AZ, USA.
Statistical Methods in Medical Research
|November 24, 2017
Summary
This study introduces a new method to correct measurement errors in serum biomarker data, improving regression analysis and variable selection for high-dimensional datasets. The approach enhances accuracy when error distributions are unknown, crucial for biomarker discovery.
Area of Science:
- Biostatistics
- Biomarker Discovery
- Epidemiology
Background:
- Multiplex assays for serum biomarkers can exhibit higher variability than single assays.
- Measurement error in biomarker data can bias regression analysis, obscuring true associations with health outcomes.
- High-dimensional biomarker data often requires advanced statistical methods like the Least Absolute Shrinkage and Selection Operator (LASSO) for variable selection.
Purpose of the Study:
- To develop and evaluate a novel bias correction method for LASSO regression in the presence of unknown measurement error in serum biomarker data.
- To improve parameter estimation and variable selection accuracy in high-dimensional biomarker studies.
- To address the limitations of existing methods that require known or costly-to-estimate measurement error distributions.
Main Methods:
- Adapted an existing bias correction approach by estimating measurement error using validation data.
- Re-measured a subset of serum biomarkers on a random subset of the study sample to estimate error.
- Applied the corrected LASSO method to simulated data and data from the Tucson Epidemiological Study of Airway Obstructive Disease (TESAOD).
Main Results:
- The proposed method significantly reduced bias in parameter estimation compared to uncorrected methods.
- Variable selection accuracy was improved, leading to more reliable identification of relevant biomarkers.
- The approach proved effective even when the measurement error distribution was unknown.
Conclusions:
- The developed bias correction method offers a practical solution for handling measurement error in high-dimensional serum biomarker data.
- This technique enhances the reliability of regression analysis and biomarker discovery in epidemiological studies.
- The findings suggest improved accuracy and efficiency in identifying significant biomarkers associated with health outcomes.
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