Research

The Biostatistics Department is involved in a wide variety of research efforts throughout the university, including at the School of Public Health and the School of Medicine. Our mission is to develop new statistical methodology to address important questions in the biological and health sciences. We bring these innovations into practice through active collaboration with applied researchers at Yale and beyond.

Research Areas

Clinical trial design and analysis (Peduzzi, Makuch, Zelterman, Lin, Gueorguieva)

Pre­clinical drug screening (Makuch)

Regulatory affairs (Makuch)

Mental health (Zhang, Gueorguieva, Lin)

Survival analysis (Zhou)

Statistical genomics (Zhao, Wang)

Applied probability (Crawford)

Genetic epidemiology (Claus, Zhao)

HIV (Makuch, Crawford, Lin)

Cancer (Claus, Zelterman, Peduzzi, Zhao, Holford, Zhou, Makuch, Lin)

Bioinformatics and computational biology (Zhao, Ma, Wang, Crawford)

Medical imaging (Zhang)

Stochastic processes (Zhao, Holford, Crawford)

Missing data (Wang, Makuch, Crawford, Gueorguieva, Lin) 

Statistical computing (Crawford)

Rare adverse events (Makuch)

Spatial statistics (Holford)

Environmental health (Holford, Lin)

High ­dimensional data analysis (Ma, Zhao)

Survey sampling (Makuch, Crawford)

Aging (Lin, Peduzzi, Zhou)

Causal inference (Lin)

Joint longitudinal and survival modeling (Gueorguieva, Lin)

Longitudinal data (Lin, Wang, Zhou, Gueorguieva)

Categorical data (Wang, Zelterman, Gueorguieva)

Gene expression (Zhao, Crawford, Wang)

Cardiovascular disease (Makuch, Lin)

Bayesian statistics (Holford, Zhao, Crawford)

Biomarkers (Zhao, Lin, Zhou)

Network analysis (Zhao, Crawford) Statistical proteomics (Zhao)

Human genetics/genomics (Wang, Zhao)

Correlated data (Wang, Zhou, Crawford, Lin, Gueorguieva)

Mixture models (Wang, Lin, Gueorguieva)

Competing risks (Zhou, Gueorguieva, Lin)

Comparative effectiveness research (Peduzzi, Makuch, Lin, Gueorguieva)

Count data (Zelterman, Crawford)

Kidney diseases (Zhou, Lin)

Statistical epidemiology (Holford, Lin)

Meta­analysis (Makuch, Lin, Gueorguieva)