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2022-23 SEMINARS

With a little help from my friends: Importance of social relationships in adulthood tested in 2 nationally-representative cohorts

2019 Seminars

From Imperfect Immunity to Vaccine Hesitancy: Evaluating the Impact of Vaccines & Vaccination Programs

2018 SEMINARS

Computational Techniques for Bayesian Adaptive Randomized Trials

The Stratified Micro-Randomized Trial Design:Sample Size Considerations for Testing Nested Causal Effects of Time-Varying Treatment

September 17, 2018

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Frank E. Harrell Jr., Vanderbilt University

1. Understand the application of 2-part models for regressions with outcomes that are non-negative.

2. Appreciate the value of administrative data on outcomes for long-term program assessments.

3. Understand the content and objectives of supported employment programs for persons with severe and chronic mental disorders.

How Could the Sufficient-Cause Model Advance Our Understanding of Etiology?

1. To learn about the link between the sufficient-cause model and the potential-outcome model;

2. To understand subtle differences between accelerating etiologic proportion and total etiologic proportion;

3. To discuss that time is an underlying notion in biomedical research.

This seminar will address the following questions:

1. What is female genital cutting and how common is it?

2. What are people's attitudes toward genital cutting in countries where it is widely practiced?

3. What are arguments for and against "medicalizing" female genital cutting as a harm reduction strategy?

1. Be able to describe and understand the different pathways whereby access to nature in urban area impacts population health.

2. Understand the challenges in characterizing exposure to natural spaces in urban areas, and to have an awareness of the current and novel methods to estimate these exposures.

3. Be informed of findings from several recent Canadian epidemiological studies, and identify priorities for further research.

2017 SEMINARS

1. An introduction to some of the major epidemiological studies of radiation exposed populations.

2. Understand how radiation exposures are assessed in occupational setting.

3. Learn about the types of health effects associated with ionizing radiation exposures.

Statistical Tools for Analyzing Network-Linked Data

1. To propose a new general cross-validation strategy for networks, based on repeatedly removing edge values at random and then applying matrix completion to reconstruct the full network.

2. To obtain theoretical guarantees for this method under a low rank assumption on the underlying edge probability matrix, and show that the method is computationally efficient and performs well for a wide range of network tasks, in contrast to previously developed approaches that only apply under specific models. Several real-world examples will be discussed throughout the talk, including the effect of friendship networks on adolescent marijuana usage, phrases that can be learned with the help of a collaboration network of statisticians as well as statistician communities extracted from a citation network.

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Bayesian Variable Selection for Multi-Dimensional Semiparametric Regression Models

1. The exposure-response relationship between an outcome and some exposures, such as some metals, can exhibit complex, nonlinear forms, since some exposures can be beneficial and detrimental at different ranges of exposure.

2. To estimate the health effects of complex mixtures we propose sparse tensor regression, which uses tensor products of marginal basis functions to approximate complex functions.

3. To induce sparsity using multivariate spike and slab priors on the number of exposures that make up the tensor factorization. We allow the number of components required to estimate the health effects of multiple pollutants to be unknown and estimate it from the data. The proposed approach is interpretable, as we can use the posterior probabilities of inclusion to identify pollutants that interact with each other. We illustrate our approach's ability to estimate complex functions using simulated data, and apply our method to a study of the association between exposure to metal mixtures and neurodevelopment.

1. To approximate power of linear and quadratic tests to varying degree of accuracy using a smaller number of key parameters.

2. To develop a mathematical framework to obtain bounds on the genetic architecture of an underlying trait given results from a genome-wide study.

3. To observe important implications for lack or a limited number of findings in many currently reported studies.

1. To further awareness of the potential contributions of public health to the development of healthy cities.

2. To become familiar with some spatial analytic and sensor-based methods of collecting relevant data on environments and health behaviors.

3. To better understand, through urban interventions, how environments translate into health profiles and health inequalities.

1. Describe impacts of cash transfers on poverty reduction and adolescent health and well-being

2. Identify pathways of impact between cash transfer programmes and adolescent health.

3. Summarize limitations of cash transfers in improving adolescent well-being and gaps in the evidence base.

Dimitra Panagiotoglou

Data for Health Services Research: The good, the not-so-good and the messy

1. Define the three types of data health services researchers regularly encounter.

2. Describe an appropriate method to study a natural experiment.

3. Discuss challenges in developing performance measures.

1. DefineÌý screening and contrast it to related concepts including case-finding and surveillance.

2. Describe two biases and two harms specific to screening.

3. Use a published framework to explain key considerations for decision-making about screening.