/tags/2012-winter/index.xml 2012 Winter - McGill Statistics Seminars

2012 Winter

  • Applying Kalman filtering to problems in causal inference

    Sepideh Farsinezhad · Jan 27, 2012

    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). However, there does not currently exist straight-forward applications of G-Estimation and MSMs for continuous treatment. In this talk, I will introduce an alternative approach to previous methods which utilize the Kalman filter. The key advantage to the Kalman filter approach is that the model easily accommodates continuous levels of treatment.

  • A concave regularization technique for sparse mixture models

    Martin Larsson · Jan 20, 2012

    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. Unfortunately concave regularization typically results in EM algorithms that must perform problematic non-convex M-step optimization. In this work, we introduce a technique for circumventing this difficulty, using the so-called Mountain Pass Theorem to provide easily verifiable conditions under which the M-step is well-behaved despite the lacking convexity. We also develop a correspondence between logarithmic regularization and what we term the pseudo-Dirichlet distribution, a generalization of the ordinary Dirichlet distribution well-suited for inducing sparsity. We demonstrate our approach on a text corpus, inferring a sparse topic mixture model for 2,406 weblogs.

  • Bayesian approaches to evidence synthesis in clinical practice guideline development

    Yulei He · Jan 13, 2012

    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. This talk describes some of our work under a contract with ACCF/AHA for applying Bayesian methods to guideline recommendation development. In a demonstration example, we use Bayesian meta-analysis strategies to summarize evidence on the comparative effectiveness between Percutaneous coronary intervention and Coronary artery bypass grafting for patients with unprotected left main coronary artery disease. We show the usefulness and flexibility of Bayesian methods in handling data arisen from studies with different designs (e.g. RCTs and observational studies), performing indirect comparison among treatments when studies with direct comparisons are unavailable, and accounting for historical data.