In addition to learning data collection and analysis methods, participants learn research collaboration efforts by engaging in group projects with other participants and graduate students. Group projects are designed and mentored by a faculty member in the Departments of Biostatistics or Epidemiology and by a graduate student or postdoctoral research fellow. This research is a good introduction to research methods, analysis, and organization and presentation of results.
Tracking the Effectiveness of Power Plant Pollution Regulations
Faculty Mentor: Cory Zigler
Postdoc Mentor: Chanmin Kim
2016 Program Participants: Janelle Walker, Joseph Zoller
Motivated by the evident link between exposure to air pollution and adverse health outcomes, the US has enacted a suite of regulatory policies designed to reduce pollution-related health burden by limiting harmful emissions from US power plants. Various compounds emitted from power plants undergo complex chemical and physical processes to form harmful air pollution that is transported across space. Thus, a defining feature of power plant regulations is that actions to reduce pollution are taken at power plants, but key questions for regulatory policy pertain to how emissions reductions unfold throughout the atmosphere to affect pollution and health outcomes across the country. Evaluating such policies requires knowledge of exactly which parts of the country experience improved air quality when specific power plants take action to reduce emissions.
This project will use combine data on 1) continuous emissions monitoring data measured at over 1000 power plants from 1997-2014 and 2) ambient pollution monitoring data at over 1000 different locations over the same time period to learn about how changes in power plant emissions manifest as changes in ambient air quality. A particular focus will be to learn where changes in air quality occur after a particular power plant reduces emissions.
Changes in Health-Related Quality of Life after Bone Marrow Transplantation for Severe Sickle Cell Disease
Faculty Mentor: Donna Neuberg
Postdoc Mentor: Kristen Stevenson
2016 Program Participants: Thabat Dahdoul, Rebekah Loving, Marcus Spearman
Adolescents and young adults with severe sickle cell disease experience extensive side effects from their disease, including frequent hospitalizations for pain and red cell transfusion, a higher risk of stroke and other cerebrovascular disease, and pulmonary and renal complications. These complications stem from an inherited mutation in the gene for hemoglobin which results in impaired oxygen transport by red blood cells. The only potentially curative therapy for this disease is allogeneic bone marrow transplantation, which also carries risks. We have conducted a pilot study of transplant in 22 patients. As a part of the study, we assessed health-related quality of life at baseline, 3 months after transplant, and 1 year after transplant. These data are now ready for analysis.
Students will describe HRQoL at baseline in the patients who were eligible for, and participated in, this study, as well as changes from baseline at the one-year time point. They will also model those changes, using the qualifying measures of disease severity as covariates. This analysis is important because a larger comparative study is about to start, and we hope that data on potential changes in HRQoL will stimulate interest in participation in, and adherence to HRQoL and functional measurements, in the next study.
Ensembles for Publicly Available Data
Faculty Mentor: Sherri Rose
Graduate Student Mentor: Savannah Bergquist
2016 Program Participants: Kimberlyn Bailey, Jarvis Miller, Valerie Santiago González
The introduction of machine learning approaches for prediction in health research has the potential to provide improved insights. Historically, these questions have been addressed using parametric regression. Ensembling allows researchers to combine multiple algorithms to build an optimal prediction function. In this project, students will explore publicly available data sets and implement ensembling using existing R packages.
Design Considerations for Two-Phase Studies in Cluster-Correlated Settings, with Application to Anti-Retroviral Treatment Programs in Resource-Limited Settings
On-going monitoring and evaluation of anti-retroviral treatment (ART) programs is critical is these programs are to be well-managed, grow and be sustained. Unfortunately, in resource-limited settings such as Malawi, a land-locked country in sub-Saharan Africa, these efforts are often limited because of a lack of detailed patient-level data. The Malawian national ART program, for example, relies primarily on data that is aggregated at the clinic level. Use of such data, however, for investigating patient-level risk factors may be subject to ecological bias. Since collecting detailed patient-level data on all patients is not feasible, financially or logistically, we recently proposed the use of the two-phase study design as a compromise. Briefly, at phase I the design uses readily-available data to stratify the patient population. At phase II detailed patient-level data is collected on a judiciously chosen sub-sample. In this project we will explore design considerations when choosing both the phase I stratification and the phase II allocation scheme. In particular, using simulations based on real data from Malawi, we will ask questions such as: how many clinics should be sampled? Given fixed resources devoted to the study, how should they be allocated across the chosen clinics? What role do the model components play? Once complete, the results from this study will be useful to researchers and planners at Ministries of Health in resource-limited settings as they consider how best to monitor and evaluate their national ART programs.
Controversy in Pharmacogenomics
In 2012, two studies (Garnett et al and Barretina et al) attempted to correlate large numbers of gene expression, mutation, and copy number measurements in hundreds of cancer cell lines with sensitivities to hundreds of different drugs, with the goal of finding genes or mutations that might indicate certain kinds of cancers with vulnerabilities to specific drugs. However, a subsequent study (Haibe-Kains et al 2013), attempting to replicate the initial findings, found major inconsistencies in the results of the two studies. We will review the papers, download the data and analyze it ourselves to form our own conclusions.