Sebastien Haneuse

My methodologic research follows two general themes, the first of which focuses on the development of novel study designs that help address bias encountered in the analysis of data from observational studies. To a greater extent, methods development in the statistical sciences focuses on post-hoc analysis techniques that attempt to overcome deficiencies in the data collection process. In many instances, however, without forethought, there may still be insufficient information in the observed data to inform the analysis technique. Examples include inferring individual-level associations from an ecological study, and the adjustment for selection bias when key components of the selection mechanism are not observed on all individuals. Without additional information one must resort to making empirically unverifiable assumptions, or to per- forming a sensitivity analysis. My research adopts a somewhat different philosophy and looks to augment the data collection process with supplementary information that can then be used to directly address the various biases. The simultaneous development of statistical tools that ensure valid and efficient estimation and inference is a crucial aspect of this research.

The second general theme of my research involves the development and use of flexible, so-called non- parametric, prior distributions for semi-parametric Bayesian analyses. Two key components of this research are (i) exploiting the flexibility of these specifications to gain additional insights into mechanisms and/or etiology, and (ii) overcoming the consequences of model misspecification, particularly in the analysis of correlated or longitudinal data.

Currently, my collaborations are in three general areas: breast cancer screening, Alzheimers' disease and dementia, and obesity.

Ph.D. Biostatistics, 2004, University of Washington.
MSc. Biostatistics, 2000, University of Washington.
MSc. Mathematics, 1998, Northern Arizona University.
BSc. Probability and Statistics, 1995, University of Sheffield.