2023 Summer Institute for Training in Biostatistics and Data Science at Columbia (SIBDS) Program
Summer Training Institute in Biostatistics and Data Science at Columbia (SIBDS@Columbia) is an innovative seven-week summer research training program where participants acquire and hone quantitative skills anchored on data immersion related to research challenges in studies of heart and lung diseases as well as infectious diseases. The seven weeks include a one-week asynchronous component during which participants will receive an introduction to biostatistics and data science as well as training on RStudio and other software.
This year's SIBDS Program brought together 10 students from around the country for the opportunity to learn and engage in research right in the heart of New York City. We are honored to have worked with such bright and enthusiastic students this past summer.
Schools Represented:
- University of Maryland - College Park
- Lehigh University
- Bryn Mawr College
- Emory University
- Bates College
- Yeshiva University
- Brown University
- Macalester College
- Wellesley College
- Columbia University
Research Projects:
Mentor: Linda Valeri, PhD, Assistant Professor of Biostatistics
Mentees: Lucy Cambefort & Malika Top
This project investigated the joint effect of correlated environmental mixtures on child neurodevelopment. Causal inference is challenged by the complexity of exposure profiles, their correlation, and confounding in the observational study. Causal inference approaches for confounding adjustment are understudied in the context of environmental mixture data. The project involved applying and comparing state-of-the art approaches for confounding adjustment and applying it to a cohort study in Bangladesh.
Mentor: Yifei Sun, PhD, Assistant Professor of Biostatistics
Mentees: Kate Brown & Faith Nwando
The biomarkers of Alzheimer's disease in cerebrospinal fluid (CSF) can change many years before the onset of clinical symptoms of mild cognitive impairment (MCI). Our study aims to examine how patterns of CSF biomarker changes differ between individuals who developed MCI and those who remained cognitively normal over time. We provided visualizations of the CSF biomarker trajectories and investigate the impact of potential risk factors on these trajectories.
Mentor: Jeanette Stingone, PhD, Assistant Professor of Epidemiology
Mentees: Emma Angell & Sunny Fong
Research suggests demographic, economic, residential, and health-related factors influence vulnerability to environmental exposures. Neighborhoods with greater vulnerability may experience more adverse health outcomes. In prior work, we developed a neighborhood environmental vulnerability index (NEVI) and found it was associated with childhood asthma outcomes in 3 US metropolitan areas. The focus of this project was to estimate whether neighborhood vulnerability also interacts with air pollution exposure to contribute to adverse childhood asthma outcomes and neighborhood-level disparities. The project utilized data from California and New York and introduced students to both index construction using a data integration framework and regression analyses in R.
Mentor: Ying Wei, PhD, Professor of Biostatistics
Mentees: Giang Thai & Jenny Rapp
The COVID-19 pandemic has had a profound impact on the world, causing significant disruptions in public health, economic stability, and social life. In the United States, the pandemic has highlighted the importance of understanding the spread of the virus and the effectiveness of various government responses in mitigating its impact. As the pandemic unfolded, different states implemented a wide range of containment measures, economic support policies, and public health interventions. Understanding the relationship between these factors and the spread of COVID-19 is crucial for informing future policy decisions and improving public health outcomes.
In this project, we explored different modeling strategies to investigate this relationship. We analyzed the COVID-19 case data and various government response indexes in the United States during the year 2020 before the vaccine became available. The government response indexes include containment measures, economic support policies, and stringency levels. We also investigated the role of demographic factors, such as population density and elderly population, in the spread of COVID-19 and the effectiveness of government responses.
This project aimed to obtain a good understanding of the factors influencing the pandemic’s impact on public health. Ultimately, the insights gained from this analysis can help inform future policy decisions and guide effective response strategies in the face of ongoing and future public health emergencies.
Mentor: Caleb Miles, PhD, Assistant Professor of Biostatistics
Mentees: Shukria Zaman & Paige Tomer
Using data from the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE), we compared the effectiveness of newer antipsychotic medications relative to older medications in their effect on health outcomes in patients with schizophrenia. We considered different approaches to adjusting for noncompliance, as the study design allowed for patients to change their medication over the course of the study. We also looked at treatment effect heterogeneity to understand whether some patients would do better on one medication and other patients on another, or if the strength of the effect varied depending on certain patient characteristics.
