Below you will find a list of preceptorship projects that faculty have offered as potential project ideas. Note that this list is not exhaustive and you may approach faculty members for other project ideas.

Preceptorship Projects Available for 2024-25

(revised June 18, 2024)

1. Potential projects with Emily Roberts
   • Project 1: Combine RCT with Observational Data – The use of historical data during randomized clinical trials (RCTs) is increasing, and there is excitement about using “real-world evidence” and observational data to improve the generalizability and efficacy of RCTs. This project would consider how data integration or other causal methods could be used to incorporate multiple datasets to evaluate treatment effects for trials with small sample sizes.
   • Project 2: Futility Stopping Rules – We are interested in looking at trial outcomes for the purpose of defining a stopping rule for futility in a clinical trial if treatment efficacy is not being demonstrated early on. This project would help determine what these stopping rules might look like with an application with prostate cancer trials.
2. Measures of diversity and equity (Jeff Dawson)
   Dr. Dawson is interested in supervising a preceptorship focusing on statistical methods in justice, equity, diversity, and inclusion (JEDI). This project would investigate existing and novel metrics of diversity, representativeness, and/or income disparities, through simulations and analysis of publicly-available data. The project will extend recent work to be presented by JEDI experts at the Joint Statistical Meetings in August of 2023, where Dr. Dawson was an official discussant.
3. Two potential projects with Kai Wang
   Please contact Dr. Wang to discuss the data use.
   3.1 Summary statistics Mendelian randomization analysis of tinnitus and hearing difficulty in noise.
   3.2 Improving polygenic scores using deep learning.
   3.3 Prediction of PCB contamination level using deep learning.
   3.4 Survival analysis of risk factors on conversion time to glaucoma.
4. R package for tumor growth experiments (Patrick Breheny)
   A common experiment in cancer research is to implant tumors in two groups of mice, then give an experimental drug to one group in order to see if it slows the growth of the tumor. The experiment is relatively simple, and yet not trivial to analyze, as it involves repeated longitudinal measurements, nonlinear trends (tumor growth tends to be exponential), and censoring (if the tumor is too small, it cannot be detected). An R package (which I personally would call “tumr”) to facilitate these analyses would be very helpful — to cancer researchers here at the University of Iowa Cancer Center but also other centers.
   Additional Breheny projects:
   • PROJECT 1: Improving tests to detect mRNA-miRNA binding sites: Modern deep sequencing methods have enabled researchers at the University of Iowa to collect genome-wide data on binding interactions between messenger RNA (mRNA) and micro RNAs (miRNA). When such an interaction is present, it leads to a build-up on reads in a particular location on the genome. The current way that these investigators test to see if such a “bump” is real, however, treats each nucleotide independently. This is flawed, however, since reads typically span many nucleotides and are thus spatially correlated. The goal of this preceptorship is to test for these interactions in a more sophisticated manner and cut down on false positives.
   • PROJECT 2: Sliding windows for combining GWAS results: To improve power, researchers often want to combine results from multiple genome-wide association studies (GWAS). One obstacle to combining results is that typically, these studies probe the genome in slightly different locations and a large number of variants are dropped from the analysis if they are not present in each study. A potential improvement is to consider instead a window of variants that slides along the genome. This would allow the two studies to combine information if their variants were nearby without requiring them to be in the exact same location. There is particular interest in applying this to a study done here at Iowa on patients with severe arrhythmia.
5. Enteric pathogens are a major source of morbidity and mortality in infants and young children living in low to middle income countries.  The pathways through which disease is transmitted are myriad and varied. Potential projects with Daniel Sewell
   • PROJECT 1: We wish to apply machine learning approaches to predict 2-week prevalence of a variety of pathogen infections in infants using structured observational data on infant and caregiver behaviors as well as environmental factors.   
   • PROJECT 2: (Predicated on someone completing PROJECT 1) We will build off of PROJECT 1 by determining the predictive capacity of each variable using Bayesian bootstrapping.
   • PROJECT 3: Often pathogens will appear in infant stools, but it may not indicate an infection.  We wish to determine the non-linear relationship between pathogen concentration and symptomology.
6. Multiple Structural Breaks Detection through Genetic Algorithm (Gideon Zamba)
   Under stimuli and workloads, the human body tends to display discomforts and covert proximal cognitions that can manifest through physiological responses. One such response can be in a form of skin sweats, easily capturable via wearable sensors. The sensors capturing electrodermal activities (EDA) record big data serially (4Hz, 8Hz), per subject, over a duration of an experiment (approximately 35 min). This project aims to study serial EDA data collected on subjects from an experiment being conducted in neuropsychology at the University of Iowa, with the goal to detect structural breaks corresponding to epochs of learning activities and assess the role that biofeedback plays in efforts to engage people in a learner space. The project mobilizes technological innovations in neuroimaging (fNIRS), wearable sensors monitoring covert cognitive activity, monitoring arousal states under workload, and video data outputs to address learners’ emotional discomfort. The project will focus on structural breaks detection though a genetic algorithm stochastic search across the spectrum of EDA data.
7. Burnout Among Servicemen: A case of the Russian-Ukraine War (Gideon Zamba)
   Studies of war veterans estimated that 95% of military members consider burnout to be the leading cause of separation, retirement, and interpersonal disorders. Burnout, defined as a syndrome of emotional exhaustion and feelings of workplace failure that occurs in response to chronic exposure to occupational stressors, if not attended to and properly addressed, can trigger, or induce emotional disorders, feeling of discouragement, frustrations, worthlessness, and depression among servicemen. The current project involves researchers from the University of Iowa and investigators in the Psychology Department at Tara Shevchenko University in Kiev, Ukraine. The data were gathered on Ukraine’s servicemen at the frontline of the Russian-Ukraine war (n = 400). Burnout Assessment Tools (BAT), Interpersonal Guilt Rating Scale Self Report (IGRS-SR), Basic Psychological Need Satisfaction and Frustration Scale measurements (BPNSFS), will be cross-studied as functions of servicemen socio-demographic characteristics such as: gender, age, education, marital status, number of children, combat operation and other characteristics. Specific hypotheses will be studied and tested, and recommendations will be made with direct policy implications regarding burnouts among frontline servicemen.
8. Understanding HIV continuum among MSM in Iowa (Gideon Zamba)
   Of the estimated 36,136 new HIV diagnoses in the US in 2021, 71% were among men who have sex with other men (MSM) including Black/African American MSM (36%). Similarly in Iowa, MSM accounted for 73% of the 120 new HIV diagnoses and 54% of the estimated 3,228 people living with HIV in 2022. According to the Iowa Department of Health and Human Services 2023 surveillance report, Non-Hispanic Black/African Americans were over 10 times more likely to be diagnosed with HIV than Non-Hispanic white Iowans. To address this disparity in HIV incidence and prevalence, one of the four strategic goals of the US government’s “Ending the HIV Epidemic” (EHE) initiative is to expand coverage of pre-exposure prophylaxis (PrEP) by 2030 especially among populations at increased risk of HIV acquisition. It is important for researchers and policy makers to understand the HIV care continuum in Iowa to be able to achieve the EHE goals. This project seeks to understand HIV care continuum among MSM across races/ethnic groups and geographical locations in Iowa.