Professor Laud joined the Division of Biostatistics in the spring of 1994. He was previously in the Department of Mathematical Sciences at Northern Illinois University. His methodological research has spanned many areas of Bayesian statistics: nonparametric models for survival analysis, model selection in linear and generalized linear models, computational methods, semi-parametric models for instrumental variables regression with binary and time-to-event outcomes.
He has several collaborative relationships with researchers at the College, particularly with those in the College's Comprehensive Injury Center (CIC), the Center for Advancing Population Science (CAPS), and the Cardiovascular Research Center. Projects include falls injury prevention, relationship of obesity and motor vehicle crash fatalities, breast cancer screening and surveillance, genetic mechanisms of hypertension development in salt sensitive rats, and many others. The projects involve both experimental and observational studies.
Dr. Laud, Professor of Biostatistics, has extensive experience in design and analysis of scientific studies in medicine, especially using large administrative databases such as SEER-Medicare linked data and Medicare billing records.
Over the past decade and a half, he has served as lead biostatistician on numerous funded research projects with investigators both within CAPS and more broadly throughout the College. Research areas have included breast cancer surveillance and survivorship, cancer prevention, bone health, cervical spine surgery outcomes, obesity and motor vehicle injury, management of diabetes and hypertension, effect of discontinuity of care on hospitalized patients, prevention of falls in the elderly, and molecular and functional regulatory networks in hypertension. His research in statistical methodology generally takes the Bayesian approach to inference, and has been published in leading statistical journals. Dr. Laud’s vast research experience includes linear and generalized linear models, model selection, Markov chain Monte Carlo, time to event data, subgroup analysis in clinical trials, Bayesian nonparametrics, and instrumental variables models for observational data in comparative effectiveness studies.