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KMID : 1155220090340010053
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Journal of the Korean Society of Health Information and Health Statistics
2009 Volume.34 No. 1 p.53 ~ p.62
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A comparison of statistical methods for adjusting the treatment effects in genetic association studies of quantitative traits
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Han Kyung-Hwa
Jang Yang-Soo
Song Ki-Jun
Lim Kil-Seob
Park Sung-Ha
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Abstract
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Objective: This paper aims to compare the performance of regression-based statistical approaches that were
currently used or advocated to adjust a treatment effect.
Methods: The six methods used to compare their relative performance were: excluding treated individuals from data, no adjustment for treatment effect, modelling treatment as a covariate(indicator variable), non-parametric adjustment of treatment, adding a constant value to measurements for treated individuals, and censored normal regression. We applied these methods to real genetic and clinical data from Yonsei cardiovascular genome center to demonstrate a pattern of their behaviour.
Results: Two of the adjustment methods were more powerful than other methods for analysis of genetic association with serum lipid profiles. These were: no adjustment to the observed lipid profiles in treated subjects, non-parametric adjustment method based on averaging ordered residuals.
Conclusion: Non-parametric adjustment method based on averaging ordered residuals and no adjustment to the observed lipid profiles in treated subjects can effectively adjust the distorting effect of lipid-lowering drug and recover a marked loss in statistical power. Also, in genetic association studies of continuous traits that distortion arising from a treatment effect really matters, we proposed to use the appropriate methods that are more effective and straightforward to implement.
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KEYWORD
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quantitative trait, genetic association, treatment effect adjustment, regression, censored regression
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