Upcoming Events

E.g., 06/29/2025

  • Multi-omics Boot Camp

    Thursday, May 29, 2025
    12:00 PM to 6:15 PM
    Online Event
    • Mailman School of Public Health
    The Multi-omics Boot Camp is a three-day intensive boot camp of seminars and hands-on sessions to provide an overview of concepts and methods used to analyze multiple omics data in observational studies. Specific topics will include integrating germline genetic, gene expression, and exposomic data, gene-environment interaction, mediation, and polygenetic risk scores for assessing risk, estimating subgroups, and selecting relevant features. This three-day intensive workshop will provide an overview of multiple approaches to analyze multiple omic data types measured on the same individuals or via the use of summary statistic data. Instructors have experience in developing and applying methods for omic analysis in genetic and environmental epidemiology and are members of an active program project focused on developing statistical methods for integrated analysis. The workshop will include seminar lectures with hands-on computer lab sessions to put concepts into practice. Since the analysis of multi-omic data is broad in scope, the workshop will survey a range of approaches and highlight the appropriate application and interpretation of each approach for specific research questions. The lab sessions will provide an opportunity to work hands-on with different types of omic data. By the end of the workshop, participants will be familiar with the following topics: Data reduction, including clustering. Regression analysis, including regularized regression, hierarchical models, and partial least squares. Interaction analysis and Genome-wide interaction scans (GWIS). Mediation analysis. Polygenic/polyexposure analysis. Integrative analysis using genome-wide association study (GWAS) summary statistics. Audience and Requirements Investigators from any institution and from all career stages are welcome to attend, and we particularly encourage trainees and early-stage investigators to participate. There are three requirements to attend this training: Each participant must have an introductory background in statistics and epidemiology. Each participant must be familiar with R. Each participant is required to have a personal laptop/computer and a free, basic RStudio Cloud account. All lab sessions will be done using RStudio Cloud.  Additional Information Subscribe for updates on new Boot Camp details and registration deadlines. Contact the Multi-omics Boot Camp team. Capacity is limited. Paid registration is required to attend.

  • Multi-omics Boot Camp

    Friday, May 30, 2025
    12:00 PM to 6:15 PM
    Online Event
    • Mailman School of Public Health
    The Multi-omics Boot Camp is a three-day intensive boot camp of seminars and hands-on sessions to provide an overview of concepts and methods used to analyze multiple omics data in observational studies. Specific topics will include integrating germline genetic, gene expression, and exposomic data, gene-environment interaction, mediation, and polygenetic risk scores for assessing risk, estimating subgroups, and selecting relevant features. This three-day intensive workshop will provide an overview of multiple approaches to analyze multiple omic data types measured on the same individuals or via the use of summary statistic data. Instructors have experience in developing and applying methods for omic analysis in genetic and environmental epidemiology and are members of an active program project focused on developing statistical methods for integrated analysis. The workshop will include seminar lectures with hands-on computer lab sessions to put concepts into practice. Since the analysis of multi-omic data is broad in scope, the workshop will survey a range of approaches and highlight the appropriate application and interpretation of each approach for specific research questions. The lab sessions will provide an opportunity to work hands-on with different types of omic data. By the end of the workshop, participants will be familiar with the following topics: Data reduction, including clustering. Regression analysis, including regularized regression, hierarchical models, and partial least squares. Interaction analysis and Genome-wide interaction scans (GWIS). Mediation analysis. Polygenic/polyexposure analysis. Integrative analysis using genome-wide association study (GWAS) summary statistics. Audience and Requirements Investigators from any institution and from all career stages are welcome to attend, and we particularly encourage trainees and early-stage investigators to participate. There are three requirements to attend this training: Each participant must have an introductory background in statistics and epidemiology. Each participant must be familiar with R. Each participant is required to have a personal laptop/computer and a free, basic RStudio Cloud account. All lab sessions will be done using RStudio Cloud.  Additional Information Subscribe for updates on new Boot Camp details and registration deadlines. Contact the Multi-omics Boot Camp team. Capacity is limited. Paid registration is required to attend.

  • Analysis of Complex Survey Data - episummer

    Sunday, June 1, 2025
    12:00 AM
    Online Event
    • Mailman School of Public Health
    Complex survey data violate typical assumptions about simple random samples of independent observations, thus requiring specialized statistical techniques. This course will provide participants with practical skills to analyze data arising from complex epidemiologic sampling designs. The theory behind complex sampling strategies and the necessity of applying appropriate statistical techniques to analyze these data and make valid inferences will be discussed. National Household Survey on Drug Use and Health (NSDUH) data will be used for applied demonstrations, illustrating concepts applicable to all datasets arising from complex survey designs. We will demonstrate the appropriate use of sampling weights in the NSDUH data and how the appropriate weight is specific to the research question being asked. We will demonstrate how to obtain basic descriptive statistics, appropriate variance estimates, and regression parameters in R (SAS and Stata code will also be provided). Copy the link below to view this course page. https://reg.learningstream.com/reg/event_page.aspx?ek=0013-0020-0ea4d403cb704d86ac9366794b2d8674  

  • Intensive Meta-Analysis - episummer

    Sunday, June 1, 2025 to Monday, June 30, 2025
    12:00 AM
    Online
    • Mailman School of Public Health
    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.

  • episummer

    Sunday, June 1, 2025 to Monday, June 30, 2025
    12:00 AM
    TBD
    • Mailman School of Public Health
    • Research
    • School of Social Work
    • Basic Health Sciences
    • Careers
    • Graduate School of Arts and Sciences
    • Natural Sciences
    • Columbia Nursing
    • College of Physicians and Surgeons
    • College of Dental Medicine
    • Clinical Health Sciences
    • Education
    • Healthcare
    Columbia University's Department of Epidemiology has a summer institute every June called episummer@columbia that enables anyone anywhere in the world to engage in the world-class coursework and high-quality instruction offered at Columbia University. It provides opportunities to gain foundational knowledge and applied skills for advancing population health research. episummer@columbia’s intensive short courses are offered in synchronous or asynchronous online learning formats. Registrants for episummer@columbia courses need access to high-speed internet. Specific information about online access is provided to registrants before the course begins. episummer@columbia: synchronous learning Our synchronous courses vary in length from 4 hours to 20 hours delivered in an online live format. These courses are offered only on a specific date and time allowing registrants the ability to interact live with their episummer@columbia course instructor and other registrants. We have a few courses that are offered in a hybrid format and one course that is fully in-person. episummer@columbia: asynchronous learning Each asynchronous course, varying in length from 5 hours to 40 hours, contains lectures and course material presented online with materials released by the instructor accordingly. The flexible format will include video or audio recordings of lecture material, file sharing and topical discussion fora, self-assessment exercises, real-time electronic office hours and access to instructors for feedback during the course.   To learn more about episummer@columbia please visit cuepisummer.org

  • Agent-Based Models for Population Health - episummer

    Sunday, June 1, 2025 to Monday, June 30, 2025
    12:00 AM
    Online Event
    • Mailman School of Public Health
    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  

  • Epidemiologic Analysis Using SAS - episummer

    Sunday, June 1, 2025 to Monday, June 30, 2025
    12:00 AM
    Online Event
    • Mailman School of Public Health
    Epidemiologic Analysis Using SAS The course will teach students to apply basic analytical epidemiologic methods using SAS statistical software. Heavy emphasis will be placed on basic data manipulation and operationalization skills in preparation for applied epidemiologic analysis that builds on concepts introduced in epidemiology and biostatistics core courses. Each session is a combination of didactic lecture and hands-on practice. Students will conduct epidemiological analyses on actual data sets, and learn the importance of data preparation and cleaning, descriptive analyses, as well as how to conduct basic statistical analyses in SAS.   Copy the link below to view this course page.  https://reg.learningstream.com/reg/event_page.aspx?ek=0013-0020-0c2e78fcf69f4a0d9b2358879114ed7e

  • Epidemiologic Methods in Global Mental Health - episummer

    Sunday, June 1, 2025 to Monday, June 30, 2025
    12:00 AM
    Online
    • Mailman School of Public Health
    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.

  • Intensive R for Epidemiologists - episummer

    Sunday, June 1, 2025 to Monday, June 30, 2025
    12:00 AM
    Online
    • Mailman School of Public Health
    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.

  • Introduction to Biostatistics - episummer

    Sunday, June 1, 2025
    12:00 AM
    Online
    • Mailman School of Public Health
    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.

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