Ying Huang is a biostatistician whose work focuses on biomarkers, which are molecular cues that can help predict the risk of disease or the effects of treatment. Analyses of biomarker studies are useful in disease screening, the identification of surrogate endpoints in clinical trials and in the selection of treatments for cancer and infectious diseases. Using data from randomized trials and observational studies, her team develops statistical methods for the design and analysis of new studies that use biomarkers. Huang and colleagues also explore ways to improve the efficiency of biomarker selection for use in clinical trials.
Ying Huang is a biostatistician whose work focuses on biomarkers, which are molecular cues that can help predict the risk of disease or the effects of treatment. Analyses of biomarker studies are useful in disease screening, the identification of surrogate endpoints in clinical trials and in the selection of treatments for cancer and infectious diseases. Using data from randomized trials and observational studies, her team develops statistical methods for the design and analysis of new studies that use biomarkers. Huang and colleagues also explore ways to improve the efficiency of biomarker selection for use in clinical trials.