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 October 3, 2024)

1. Potential project with Emily Roberts
   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. Four 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. 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.
6. 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.
7. Grant Brown Project
   People in recovery from Alcohol Use Disorder (AUD) have a wide range of experiences and employ a diverse array of strategies in the maintenance of their recovery status and pursuit of overall wellbeing. Previous studies have identified correlates of successful long-term recovery, but the process is still poorly understood overall, particularly for cohorts in “independent” recovery who have not sought or received medical or support-group assistance. In this project, we will study the process of recovery by comparing two modelling approaches: Bayesian Vector Autoregression and Bayesian state-space compartmental models on a cohort of survey participants followed for multiple years. We will make heavy use of simulation to develop and validate the models in question.
8. Cancer research project (Grant Brown)
   It has been widely reported that cancer rates in Iowa have been either increasing or failing to improve at the rate expected relative the rest of the USA over the past several years. This is a challenging problem from a statistical/epidemiological level, because “cancer” refers to a diverse class of conditions, and arises based on a lifetime of exposures and predispositions. In addition, the data available to researchers on cancer incidence and exposures are imperfect and often highly aggregated. In this project, we’ll attempt to build on current work using spatial models to understand the distribution of cancer burden within and beyond the state of Iowa in order to pursue explicitly cumulative models of exposure risk. A particular focus will be on radon and lung cancer, and we will conduct the work in a Bayesian context.  
9. Assessing the Impact of Regional Interventions on Health Disparities – A Stroke Triage Case Study (Brown)
   When responding to emergency calls for potential stroke events, EMS must make key decisions rapidly concerning what healthcare facility to transport patients to. Crucially, different potential underlying stroke conditions have competing and time-sensitive treatment requirements. Our team has developed a Bayesian decision approach to stroke triage, as well as a nationwide simulation tool to assess the potential impact of the algorithm relative to the alternatives (i.e., guideline adherent care, wild-type care). In this project, we will develop both visual and analytical methods to assess and communicate the expected impact specifically in geographically and demographically vulnerable populations.
10. Exploring drivers of Iowa’s increasing cancer rates (Jacob Oleson)
    Iowa has the second highest cancer incidence in the United States. Using data from the Iowa Cancer Registry, we will examine longitudinal incidence rates by cancer site. We aim to determine the main cancer sites contributing to the difference in rates between Iowa and the U.S. We will determine the main geographic areas of Iowa contributing to the difference in rates between Iowa and the U.S. We will also examine which risk factors are most associated with increases in cancer rate.
11. An Investigation of the role of GLP-1 Receptors in Vision Loss: The Case of NAION patients (Gideon Zamba)
    Glucagon-like peptide-1 (GLP-1) receptor agonists are a class of anorectic drugs that help reduce blood sugar and energy intake by activating the GLP-1 receptor. GLP-1 receptor agonists are being used for weight loss and diabetes and are becoming the most widely used medications for these conditions. Recent research provided some evidence of an increased risk of non-arteritic anterior ischemic optic neuropathy (NAION) in patients using GLP-1 receptor agonists. NAION is a condition that is easily associated with the risk of a permanent vision loss. However, clinicians are not settled on the underlying causal mechanism of NAION. Although there have been suggestions that NAION conditions are linked to abrupt decrease in blood flow toward the optic nerve, there appears to be a paucity of research work in support of this hypothesis. Is there any viable association between NAION and GLP-1 receptor agonists? Are subjects under GLP-1 more likely to develop visual loss? The current research study will embark on an exploratory retrospective chart review of the record of n = 400 NAION patients, their pattern of vision loss if ever, their visual imaging data, their pattern of  GLP-1 receptor agonists usage or lack thereof, their health history, their diabetics and obesity status, and other prognostic factors that can contribute toward elucidating on time to visual loss in both with or without GLP-1 receptor agonists usage. The project will mix time to event modeling and image analysis to explore this potential association amidst a host of prognostic factors.