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 2023-24

(revised January 10, 2024)

1. Assessing longitudinal surrogate markers (Emily Roberts)
   In randomized clinical trials, an intermediate marker may serve as surrogate for a clinical outcome with the goal of making the trial run more efficiently. This proposed project will use causal inference approaches to determine if a surrogate outcome is valid to use as the primary endpoint in a future trial. Specifically, we will assess how well an extension of methods using linear or generalized linear mixed models performs when the marker is measured longitudinally (as it is in a diabetes clinical trial motivating this work). This project could also involve comparison of existing methods via simulation or other surrogate marker questions.
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.
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.