Courses

The course will be conducted as a seminar with readings and discussions on a range of more advanced topics. We will cover case-control designs; longitudinal causal models, identifiability and estimation; direct and indirect effects; dynamic regimes (individualized treatment rules); approaches for diagnosing and responding to violations in the positivity assumption. Additional topics may include stochastic interventions, community-based interventions, and Collaborative-TMLE. There will also be some guest lectures and presentations from current students and faculty members.

Andrew N Mertens, Jordan Matthew Roiland, Sarah Elisabeth Chin, Sita Manasa Susarla

This introductory graduate course presents principles & methods of epidemiology, including descriptive & analytic approaches to assessing the distributions of health, disease & injury in the population & factors influencing those distributions. Emphasis is on developing an understanding of concepts, rather than quantitative methods. Basic calculations are involved. Course consists of readings, critical review of epidemiology papers, brief video lectures to explain key concepts, quizzes & exams that allow students to practice applying epidemiological concepts. Students without prior coursework in epidemiology will acquire the core competencies in epidemiology expected of all MPH graduates. Course shares the same content & learning of PH 250A

In this course, students will study biostatistical concepts and modeling relevant to the design and analysis of multifactor population-based cohort and case-control studies, including matching. Key topics include: measures of association, causal inference, confounding interaction, with modeling focusing on logistic regression.

PBHLTH 241 001 - Intermediate Biostatistics for Public Health 

In this course, students will study biostatistical concepts and modeling relevant to the design and analysis of multifactor population-based cohort and case-control studies, including matching. Key topics include: measures of association, causal inference, confounding interaction, with modeling focusing on logistic regression.

Laura Balzer

With the ongoing “data explosion”, methods to delineate causation from correlation are perhaps more pressing now than ever. This course will introduce a general framework for Causal Inference in Public Health: 1) clear statement of the research question, 2) definition of the causal model and effect of interest, 3) assessment of identifiability, 4) choice and implementation of estimators including parametric and non-parametric methods, and 5) appropriate interpretation of findings. The statistical methods include G-computation, inverse probability weighting (IPW), and targeted minimum loss-based estimation (TMLE) with machine learning.

PBHLTH C240B 001 - Biostatistical Methods: Survival Analysis and Causality

PH 243A teaches students to construct efficient estimators & obtain robust inference for parameters that utilize data-adaptive estimation strategies (i.e., machine learning). Students perform hands-on implementation of novel estimators using high-dimensional data structures, providing students with a toolbox for analyzing complex longitudinal, observational & randomized control trial data. Students learn & apply the core principles of the Targeted Learning methodology, which generalizes machine learning to any estimand of interest; obtains an optimal estimator of the given estimand, grounded in theory; integrates state-of-the-art ensemble machine learning techniques; & provides formal statistical inference in confidence intervals & testing.

PBHLTH W250 001 - Epidemiologic Methods I

Zachary Butzin-Dozier

This introductory graduate course presents principles & methods of epidemiology, including descriptive & analytic approaches to assessing the distributions of health, disease & injury in the population & factors influencing those distributions. Emphasis is on developing an understanding of concepts, rather than quantitative methods. Basic calculations are involved. Course consists of readings, critical review of epidemiology papers, brief video lectures to explain key concepts, quizzes & exams that allow students to practice applying epidemiological concepts. Students without prior coursework in epidemiology will acquire the core competencies in epidemiology expected of all MPH graduates. Course shares the same content & learning of PH 250A.

PBHLTH W250B 001 - Epidemiologic Methods II

This course is an intermediate level course in epidemiology. It replaces previously approved and delivered courses PHW250F and PHW250G. Topics include causal inference; measurement of disease rates; inferential reasoning; research study designs, ecologic, case-control, cohort, intervention trials, meta-analytic designs; potential sources of bias, confounding, effect modification in research design are explored in depth; topics in clinical epidemiology, likelihood ratios, receiver operator curves, the sensitivity, specificity, predictive value of a test; brief introduction to logistic regression and survival analysis. Topics are covered at a advanced level than PH250A or PHW250. Readings from this course provide a firm foundation for PH250C.