Brady Thomas West

Brady Thomas West is a Research Professor at the University of Michigan with joint appointments in the Department of Biostatistics at the School of Public Health and the Survey Research Center at the Institute for Social Research. His work bridges survey methodology, applied statistics, and public health research, providing technical expertise globally on research design and statistical analysis.

Education:

• PhD in Survey Methodology, University of Michigan-Ann Arbor, 2011
• MA in Applied Statistics, University of Michigan-Ann Arbor, 2002
• BS in Statistics, University of Michigan-Ann Arbor, 2001

West's research focuses on critical challenges in survey methodology including measurement error in auxiliary variables and survey paradata, selection bias in non-probability samples, responsive/adaptive survey design, interviewer effects, and multilevel regression models for complex clustered and longitudinal data. His work directly addresses data quality issues in public health and social science research.

His publication record (2016-2022) demonstrates sustained contributions to methodological advancements through influential textbooks on linear mixed models and survey data analysis, alongside high-impact journal articles. Key themes include improving survey estimation techniques, developing frameworks for assessing analytic errors, and synthesizing evidence on interviewer effects. His research consistently bridges theoretical innovation with practical applications in aging studies, public health surveillance, and social epidemiology.

Scientific Awards: No specific awards mentioned in the provided information.

West actively leads and contributes to major funded research initiatives including the Health and Retirement Study (as Associate Director), the Network for Innovative Methods in Longitudinal Aging Studies (as MPI), and the MAPS Center for SUD/HIV research. He also develops educational resources through Coursera specializations in Statistics with Python and Total Data Quality, demonstrating commitment to training the next generation of data scientists.

He collaborates extensively through the Survey Research Center and Department of Biostatistics, contributing to teams focused on longitudinal aging studies, public health data infrastructure, and methodological innovation. His leadership in the Health and Retirement Study and NIMLAS network positions him at the forefront of aging research methodology.