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A multiple imputation method based on weighted quantile regression models for longitudinal censored biomarker data
MinJae Lee1, Mohammad H Rahbar2,3, Matthew Brown4
1Division of Clinical and Translational Sciences, Department of Internal Medicine, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, Texas, USA. MinJae.Lee@uth.tmc.edu.
BMC Medical Research Methodology
|January 13, 2018
Summary
This study introduces a new multiple imputation (MI) method using weighted censored quantile regression (CQR) to accurately analyze biomarker data with censoring and missing values. The approach improves statistical validity for disease biomarker evaluation.
Area of Science:
- Biostatistics
- Longitudinal Data Analysis
- Biomarker Research
Background:
- Biomarker data in patient studies often present challenges like left censoring due to detection limits and missing data.
- Improper handling of censored or missing longitudinal biomarker data can lead to biased parameter estimates in regression analyses.
- Accurate statistical modeling is crucial for reliable biomarker evaluation in clinical research.
Purpose of the Study:
- To develop and evaluate a novel multiple imputation (MI) strategy for longitudinal biomarker data.
- To address issues of data censoring and missingness, particularly at early study visits.
- To improve the accuracy of parameter estimates in longitudinal regression models involving biomarker covariates.
Main Methods:
- A specific multiple imputation (MI) strategy based on weighted censored quantile regression (CQR) was developed.
- Simulation studies were conducted to assess the performance of the imputation approach under various censoring levels and covariance structures.
- The method was applied to the Prospective Study of Outcomes in Ankylosing Spondylitis (PSOAS) data, focusing on C-reactive protein (CRP) levels.
Main Results:
- Simulation results demonstrated that the proposed MI-CQR method exhibited higher relative efficiency compared to other MI techniques.
- The developed approach showed robustness to different covariance structures, unlike methods assuming data normality.
- Analysis of PSOAS data revealed a significant association between higher CRP levels and radiographic damage when using the proposed imputation method, an association not found with other methods.
Conclusions:
- Multiple imputation based on weighted censored quantile regression provides a statistically valid method for analyzing biomarker data with censoring and missing early-visit data.
- This approach enhances the reliability of biomarker evaluation in longitudinal studies.
- The findings highlight the importance of appropriate statistical methods for handling complex data structures in clinical research.
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