Optimal Combining Outcomes to Improve Prediction: Candidate's seminar for the Assistant Professor in Biostatistics position (JPF01102)

Seminar | January 26 | 4:10-5 p.m. | 1011 Evans Hall

 Dr. David Benkeser, Postdoctoral Scholar, Division of Biostatistics

 Public Health, School of

Please join us! The Division of Biostatistics is delighted to host guest speaker and candidate for the Assistant Professor in Biostatistics position (JPF01102),
Dr. David Benkeser.

ABSTRACT: In many studies, multiple instruments are used to measure different facets of an unmeasured outcome of interest. For example, in studies of childhood development, children are administered tests in several areas and researchers combine these test scores into a univariate measure of neurocognitive development. Researchers are interested in predicting this development score based on household and environment characteristics early in life in order to identify children at high risk for neurocognitive delays. We propose a method for estimating the combined measure that maximizes predictive performance. Our approach allows modern machine learning techniques to be used to predict the combined outcome using potentially high-dimensional covariate information. In spite of the highly adaptive nature of the procedure, we nevertheless obtain valid estimates of the prediction algorithm’s performance for predicting the combined outcome as well as confidence intervals about these estimates. We illustrate the methodology using longitudinal cohort studies of early childhood development.