Statistical Methods for Biomarkers in Diagnostics and Prediction
Biomarkers are often for diagnosis or prediction of disease. My research focuses on developing efficient statistical methods to evaluate the effectiveness of biomarkers in diagnosis and prediction.
I have been a member of the Biostatistics and Bioinformatics Branch since 2002. I received my doctorate degree from the University of Rochester in 1997. My research interests include: general statistical estimation theory, sequential methodology and adaptive designs with applications to clinical trials and other medical studies, linear models and regression analysis, analysis of repeated measurements and longitudinal data, multivariate data analysis and related topics, statistical methods for biomarkers including diagnostic biomarkers and ROC curve analysis, statistical methods for pooled assessments, and measurement errors.
- Liu C, Liu A, Hu J, Yuan V, Halabi S. Adjusting for misclassification in a stratified biomarker clinical trial. Stat Med. 2014;33(18):3100-13.
- Wu M, Shu Y, Li Z, Liu A. Repeated significance tests of linear combinations of sensitivity and specificity of a diagnostic biomarker. Stat Med. 2016;35(19):3397-412.
- Kang L, Liu A, Tian L. Linear combination methods to improve diagnostic/prognostic accuracy on future observations. Stat Methods Med Res. 2016;25(4):1359-80.
- Yin L, Diao G, Liu A. A semiparametric method for comparing the discriminatory ability of biomarkers subject to limit of detection. Stat Med. 2017;36(26):4141-4152.
- Zhang W, Yang L, Tang LL, Liu A, Mills JL, Sun Y, Li Q. GATE: an efficient procedure in study of pleiotropic genetic associations. BMC Genomics. 2017;18(1):552.
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This page was last updated on Wednesday, July 26, 2017