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Meta-Analysis is the process of synthesizing data from multiple studies to yield a more complete understanding of the effect. It is the core of evidence-based medicine and epidemiology. The goal of this course is to teach students how to perform a meta-analysis, to interpret the results properly, and to present the results in a manner that is clear and intuitive. The techniques that we discuss in these workshops are relevant to any field where a meta-analysis might be employed. Participants typically work in such fields as medicine, epidemiology, health sciences, biostatistics, nursing, veterinary medicine, psychology, education, criminal justice, ecology, marketing, among others. Basic course (20 hours) June 1-15 In the basic course we explain how to conduct and report the results for a meta-analysis. In an analysis where the effect size is consistent across studies the goal will be to identify that common effect size and consider the clinical implications of the findings. In an analysis where the effect size varies across studies the goal will be to identify the mean effect size and also to quantify the variation in effects and consider the clinical implications of this variation: Is it the case that the treatment's effect varies from moderate to large; or from minor to moderate; or is the treatment helpful in some cases but harmful in others? Advanced course (20 hours) June 16-30 In the advanced course we revisit the same datasets we had used in the basic course. Now, rather than simply quantify the heterogeneity in effects, we try to identify factors that may explain that heterogeneity. Specifically, we use subgroup analysis (analogous to ANOVA) to compare the effect size in different sets of studies. And we use meta-regression (analogous to multiple regression) to identify the unique impact of any covariates on the effect size. We also explore other issues in meta-analysis such as publication bias; limitations of the random-effects model; and options for working with a small number of studies. Intensive course (40 hours) June 1-30 The intensive course is simply the basic course plus the advanced course. The basic course runs from June 1-15 and the advanced course runs from June 16-30.

This course will provide an introduction to agent-based models and their application to population health research and health policy. Agent-based models utilize stochastic computer simulations to observe the population-level outcomes and patterns of behavior produced when heterogeneous agents interact with other agents and their environment according to preset rules. These models circumvent many of the limitations of traditional analytic approaches and are increasingly used to investigate the spread of health behaviors and outcomes, as well as to compare interventions and policies to promote population health. Topics covered will include the properties of agent-based models, including illustrations from the health and social sciences; the types of questions best answered by agent-based models; the steps involved in developing, calibrating, and validating agent-based models; and the presentation and interpretation of model results. NetLogo software will be used to demonstrate and practice agent-based modeling techniques. Participants will also have the opportunity to propose and receive feedback on an agent-based model addressing their own research question of interest. Copy the link below to view this course page.  https://reg.learningstream.com/reg/event_page.aspx?ek=0013-0020-d4175de480e54f6182aeeab4f7113d6e  

The field of global mental health exists in large part because of a substantial disparity: low and middle-income countries (LMICs) in the Global South comprise over 80% of the global population but have less than 20% of mental health service resources and an estimated 90% of people living in LMICs do not receive adequate care. As the field has emerged, challenges in how to appropriately conduct public mental health research in LMIC contexts have surfaced. Such challenges require the appropriate application of epidemiologic methods in order to accurately measure and describe mental health and substance use problems in LMIC and evaluate and implement intervention approaches. Epidemiologic methods to be discussed in this course include: complex survey designs to measure psychiatric disorder prevalence in humanitarian and emergency settings; validation of mental health screening tools in the absence of a gold standard criterion among culturally diverse populations; cohort and case control global mental health studies; evaluation of mental health intervention effectiveness using experimental and non-experimental designs; novel methods for assessing clinical competency and intervention fidelity of lay mental health providers in LMIC; and implementation science tools, designs, and analysis approaches for translating evidence-based interventions into practice in LMIC.

This goal of this course is to introduce students to advanced data wrangling techniques, quantitative methods, and graphics using R. Students will be introduced to base R (including object types, indexing, and the apply family of functions) before moving into the Tidyverse.Heavy emphasis will be placed on the tidyverse suite of packages and functions for data manipulation and modeling, for more advanced applied epidemiologic analyses. Each session is a combination of didactic lecture and hands-on practice. Students will clean and analyze actual data sets, and learn the importance of data preparation and cleaning, how to visualize descriptive analyses, write their own functions, run regressions, and use R for advanced methods such as multiple imputation.