Research

Mentor: Alan Cohen, PhD – Associate Professor, Environmental Health Sciences
Mentees: Shirley Toribio; Hunter Farnham

We hypothesize that bodies in good health have unique signatures for communication between various systems. We have a dataset of 40 individuals with severe mitochondrial disease (unhealthy) and 70 controls (healthy), each subjected to a series of stressors, with continuous recordings of blood pressure, heart rate, and respiration. Informed by complex systems theory, we will extract signatures of what healthy communication among these time series looks like across the stress time course, and how it may differ in unhealthy individuals. Techniques used may include transfer entropy and multivariate extensions of heart rate variability. Students will learn programming in R and stats.

Mentor: Caleb Miles, PhD – Assistant Professor, Biostatistics
Mentees: Gabriella Nieves; Sarah Wu; Andrew Ghastine

Using data from the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE), we will compare the effectiveness of newer antipsychotic medications relative to older medications in their effect on health outcomes in patients with schizophrenia. We will consider different approaches to adjusting for noncompliance, as the study design allowed for patients to change their medication over the course of the study. We will also look 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.

Mentor: Jeanette Stingone, PhD – Assistant Professor, Epidemiology
Mentees: Lise Augustin; Sophia Kop; Emily Zhang

Scientific researchers have acknowledged that studies which seek to address the combined impacts of multiple environmental exposures are needed to more closely replicate human experience. However, the lack of known patterns in exposure to various chemical classes in representative and diverse populations is a fundamental block for the progression of research. This project will use existing data from NHANES, the representative biomonitoring program within the US population, to investigate the patterns in biomarkers of multiple classes of chemicals seen in pregnant people and determine whether these patterns vary by individual-level socioeconomic characteristics and are associated with gestational diabetes. Students will learn clustering techniques, create visualizations, such as correlation globes, to compare patterns across populations and conduct regression-based statistical analyses.

Mentor: Molei Liu, PhD – Assistant Professor, Biostatistics
Mentees: Lucy Liu; Kejin Dong

The first goal of this project is to build accurate and time-specific risk prediction (phenotyping) models for a broad set of age-related diseases based on longitudinal and structural features in electronic health records (EHR). Based on the derived phenotypes, our next aim is to perform a genetic association study leveraging the EHR-linked biobank data, to detect useful biomarkers in characterizing the biological aging process. Note: the mentor will highlight the training on the sense and fundamental skills in statistics and data science, through a combination of doing research and learning related materials.

Mentor: Wenpin Hou, PhD – Assistant Professor, Biostatistics
Mentees: Alexandra Duta; Matthew Eichner

Building on our prediction model, we will apply it to bulk, single-cell, and spatial RNA-seq data from public resources such as ENCODE, GTEx, Human Cell Atlas, and Recount2 to reconstruct the DNA methylation (DNAm) landscape. This effort will result in the creation of a comprehensive DNAm database encompassing various tissues, cell types, disease states, treatment conditions, and spatial locations.

The predicted database will be made publicly available, accompanied by visualization tools to facilitate data exploration. We will implement a user-friendly online interface to host the DNAm data, enabling researchers to gain deeper insights into gene regulation. This resource aims to enhance our understanding of epigenomic mechanisms and improve strategies for epigenomic therapy and precision treatment.

Mentor: Xin Ma, PhD – Assistant Professor, Biostatistics
Mentees: Alisha Bhatia; Zoe Curtis; Vivian Ferrigni

Cancer cachexia, characterized by weight loss and decline in muscle mass, is a poor prognostic factor among patients with lung cancer. Osimertinib is the standard of care for patients with metastatic EGFR-mutant non-small cell lung cancer. In previous work, we found that patients with weight loss had significantly worse overall survival. The focus of this project is to further identify subgroups of lung cancer patients with different trajectories of weight loss and compare the overall survival among these subgroups. We will provide visualization of weight loss trajectories. Students will learn about the latent trajectory analysis and hypothesis testing procedure in R.

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.