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Regression analysis with a misclassified covariate from a current status observation scheme
Leilei Zeng1, Richard J Cook, Theodore E Warkentin
1Department of Statistics and Actuarial Science, Simon Fraser University, Burnaby, British Columbia V5A 1S6, Canada. lzeng@sfu.ca
Biometrics
|August 4, 2009
Summary
Misclassified seroconversion status in regression models can cause bias. A new likelihood-based method accurately estimates covariate effects, reducing bias in deep vein thrombosis risk prediction.
Area of Science:
- Biostatistics
- Epidemiology
- Medical Statistics
Background:
- Misclassified covariates in regression models lead to inconsistent covariate effect estimation.
- Existing methods often require validation samples or replication studies.
- Orthopedic studies investigate serological response and deep vein thrombosis (DVT) risk.
Purpose of the Study:
- To develop a likelihood-based regression approach for handling misclassified seroconversion status.
- To account for early testing leading to misclassification in seroconversion time.
- To reduce bias in estimating the effect of serological response on DVT risk.
Main Methods:
- Developed a parametric and nonparametric likelihood-based approach.
- Incorporated a current status observation scheme (Case I interval censoring) for seroconversion time.
- Applied the method to orthopedic study data and conducted simulation studies.
Main Results:
- The proposed method significantly reduces bias compared to naive analyses.
- Simulation studies demonstrate the effectiveness of the likelihood-based approach.
- Application to orthopedic data illustrates practical utility in DVT risk prediction.
Conclusions:
- The developed likelihood-based method effectively addresses seroconversion status misclassification.
- This approach improves the accuracy of covariate effect estimation in regression models.
- The findings have implications for analyzing thrombotic conditions and related risk factors.
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