Within the context of policy analysis, this pilot project will assess the current state of statistical methods and computational algorithms for meta-analysis of time-to-event data, prescribe improvements for current practice, and carry-out preliminary development and implementation of these improvements. The research will make advances in statistical science that are of broad interest and practical relevance to scientists and researchers involved in statistical modeling and inference in the policy areas of education, law, health, employment, housing, poverty, crime, and economics. The assessment and development work will focus on the following: 1) Meta-analysis of time-to-event studies, including use of complete data and incorporating statistical output into a decision-theoretic framework; 2) Bayesian hierarchical modeling of time-to-event studies, introducing new theoretical and computational algorithms for the application of these models; and 3) Stochastic simulation and computer algorithms for implementation of simulation-based Bayesian time-to-event models. In addition to developing innovative time-to-event models for meta-analysis of broad interest to the statistical sciences, the project will contribute to advances in cross-disciplinary applications through collaborations with disciplinary policy analysts and through initiation of students of statistics into such project areas, thus contributing to cross-disciplinary fertilization via research and education.
