The PRIISM seminar series consists of research seminars of interest to an applied statistics audience, from innovative applications of applied statistics to novel statistical theory and methodology. An archive of past events (2008 - 2015) and (2015 - 2018) are available, as well as a list of related seminars.

Our seminar series generally consists of three categories of talks: Statistical Methodology, Didactic, and Data and Social Impact seminars. The statistical methodology talks cover new advances in methods for analyzing data, and may be more technical. Didactic seminars provide an opportunity to learn a new area of statistics or way of analyzing data. Data and social impact lectures take a critical look at how we can use data to learn about and provide recommendations for solutions to social problems through quantitative research. These talks vary in technical level.

Upcoming and Recent Events: Fall 2018 and Spring 2019

Date, Time, LocationTalk CategorySpeaker Name, AffiliationTopic (click for more info)
10/24/2018 (Weds)
11:00 am - 12:00 pm,
Kimball 3rd Fl Conf Rm 
Statistical Methodology/Didactic  Leslie McClure (Drexel)
Abstract: Planning for randomized clinical trials relies on assumptions that are often incorrect, leading to inefficient designs that could spend resources unnecessarily. Recently, trialists have been advocating for implementation of adaptive designs, which allow researchers to modify some aspect of their trial part-way through the study based on accumulating data. In this talk, I will introduce the concept of adaptive designs and describe several different adaptations that can be made in clinical trials. I will then describe a real-life example of a sample size re-estimation from the Secondary Prevention of Small Subcortical Strokes (SPS3) study, describe the statistical impact of implementing this design change, and describe the effect of the adaptation on the practical aspects of the study.
10/31/2018 (Weds)
10:30 am - 12:00 pm,
Kimball 3rd Fl Conf Rm 
Didactic  Chuck Huber (Stata Corp)
Abstract: This talk introduces the concepts and jargon of structural equation modeling (SEM) including path diagrams, latent variables, endogenous and exogenous variables, and goodness of fit. I demonstrate how to fit many familiar models such as linear regression, multivariate regression, logistic regression, confirmatory factor analysis, and multilevel models using -sem-. I wrap up by demonstrating how to fit structural equation models that contain both structural and measurement components.

11/14/2018 (Weds)
11:00 am - 12:00 pm
Kimball 3rd Fl Conf Rm 

Data and Social Impact  Ravi Shroff (NYU)
Abstract: Policymakers often seek to gauge discrimination against groups defined by race, gender, and other protected attributes. A common strategy is to estimate disparities after controlling for observed covariates in a regression model. However, not all relevant factors may be available to researchers, leading to omitted variable bias. Conversely, controlling for all available factors may also skew results, leading to so-called "included variable bias". We introduce a simple strategy, which we call risk-adjusted regression, that addresses both concerns in settings where decision makers have clear and measurable policy objectives. First, we use all available covariates to estimate the expected utility of possible decisions. Second, we measure disparities after controlling for these utility estimates alone, omitting other factors. Finally, we examine the sensitivity of results to unmeasured confounding. We demonstrate this method on a detailed dataset of 2.2 million police stops of pedestrians in New York City.

12/5/2018 (Weds)
11:00 am - 12:00 pm
Kimball 3rd Fl Conf Rm 

Statistical Methodology   Russell Steele (McGill)
Abstract: Every statistical analysis requires at least some subjective or untestable assumptions. For example, in Bayesian modelling, the analysis requires specification of hyperparameters for prior distributions which are either intended to reflect subjective beliefs about the model or to reflect relative ignorance about the model under a certain notion of ignorance. Similarly, causal models require assumptions about parameters related to unmeasured confounding. Violations of these untestable or subjective assumptions can invalidate the conclusions of analyses or lead to conclusions that only hold for a narrow range of choices for those assumptions. Currently, researchers compute several estimates based on either multiple “reasonable” values or a wide range of “possible” values for these inestimable parameters. Even when the dimension of the inestimable parameter space is relatively small, the sensitivity analyses generally are not systematically conducted and may either waste valuable computational time on choices that lead to roughly the same inference or will miss examining values of those parameters that would change the conclusions of the analysis.

In this talk, I will propose the use of Bayesian optimization approaches for decision-driven sensitivity analyses. We assume that a decision will be made as a function of the model estimates or predictions from particular model which relies on inestimable parameters. We use a Bayesian optimization approach to identify partitions of the space of inestimable parameter values where the decision based on the observed data and assumed parameter values change, rather to rely on non-systematically chosen values for the sensitivity analysis. We will illustrate our proposed approach on a hierarchical Bayesian meta-analysis example from the literature.

The work that will be presented was done in collaboration with Louis Arsenault-Mahjoubi, an undergraduate mathematics and statistics student at McGill University.

2/6/2019 (Weds)
11:00 am - 12:00 pm
Kimball 3rd Fl Conf Rm 

TBA   Melody Goodman (NYU)
Abstract: TBA

2/27/2019 (Weds)
11:00 am - 12:00 pm
Kimball 3rd Fl Conf Rm 

TBA   Eric Loken (UConn)
Abstract: TBA

4/3/2019 (Weds)
11:00 am - 12:00 pm
Kimball 3rd Fl Conf Rm 

TBA   Gregory Ridgeway (UPenn)
Abstract: TBA

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