2012 Winter

Date: 2012-01-27 Time: 15:30-16:30 Location: BURN 1205 Abstract: A common problem in observational studies is estimating the causal effect of time-varying treatment in the presence of a time varying confounder. When random assignment of subjects to comparison groups is not possible, time-varying confounders can cause bias in estimating causal effects even after standard regression adjustment if past treatment history is a predictor of future confounders. To eliminate the bias of standard methods for estimating the causal effect of time varying treatment, Robins developed a number of innovative methods for discrete treatment levels, including G-computation, G-estimation, and marginal structural models (MSMs).

Date: 2012-01-20 Time: 15:30-16:30 Location: BURN 1205 Abstract: Latent variable mixture models are a powerful tool for exploring the structure in large datasets. A common challenge for interpreting such models is a desire to impose sparsity, the natural assumption that each data point only contains few latent features. Since mixture distributions are constrained in their L1 norm, typical sparsity techniques based on L1 regularization become toothless, and concave regularization becomes necessary.

Date: 2012-01-13 Time: 15:30-16:30 Location: Concordia, Library Building LB-921.04 Abstract: The American College of Cardiology Foundation (ACCF) and the American Heart Association (AHA) have jointly engaged in the production of guideline in the area of cardiovascular disease since 1980. The developed guidelines are intended to assist health care providers in clinical decision making by describing a range of generally acceptable approaches for the diagnosis, management, or prevention of specific diseases or conditions.