As an AUA Medical Student Fellowship recipient, I am excited to explore the long-term urinary adverse events (UAEs) that prostate cancer survivors face. This work is crucial, considering prostate cancer’s high survivability and prevalence as the second most common cancer among men globally.¹ As survival rates rise with improved treatments and earlier diagnosis, it is critical to understand adverse events related to prostate cancer treatment since these can significantly impair quality of life for patients.^2,3 Our research is poised to enhance our knowledge of the long-term effects of various prostate cancer treatments, especially the UAEs that can appear years to decades later.⁴

Our project will use SEER (Surveillance, Epidemiology, and End Results)-Medicare data from the past 2 decades to evaluate the cumulative incidence and risk of UAEs, applying propensity scores and inverse probability of treatment weighting to adjust for confounding variables.⁵ A key focus will be on racial disparities that may impact posttreatment outcomes. Research suggests that factors such as socioeconomic status, access to health care, and variances in continuity of care may contribute to differences in posttreatment complications.^6-8 Through analyzing long-term follow-up data, our goal is to address possible underreporting of UAEs with delayed onset and to identify trends across different treatment and demographic groups.⁹ By uncovering the patterns of long-term UAEs, our findings could motivate more personalized follow-up care protocols, influence the allocation of resources for survivorship programs, and inform preemptive measures to improve quality of survival for prostate cancer patients.

Receiving this fellowship fuels my ambition to pursue a career combining urology and data science. I am grateful for the opportunity to train at the University of California San Francisco (UCSF) with Dr Benjamin Breyer and to be part of a research team adept in applying large data methods to clinical questions, supported by a university with unmatched computational and collaborative resources. As part a year off between my third and fourth years of medical school, I have chosen to pursue a master’s in clinical research with data science specialization at UCSF. I believe this training is crucial for leading future urological health care innovations driven by data.

In honing my skills, I am cognizant of the ethical considerations of using big data tools like machine learning and artificial intelligence to address clinical questions. Data misuse can amplify health inequities and deepen biases. For example, machine learning models trained on biased datasets may exacerbate higher rates of prostate cancer mortality in Black patients. My training in clinical research and epidemiology will empower me to correct biases and discern causation from correlation. My involvement in this project underlines my commitment to better understand how to reliably derive causal inferences from large and biased datasets to ultimately build a better health care system for all.

This fellowship represents more than a professional achievement; it acknowledges the profound impact of my mentors and the support system behind me. My educational and personal journey reflect the public education I have been fortunate to receive and the dreams of my immigrant parents, which I have carried forward in my own. From my first urology mentor, Dr Christopher Saigal at the University of California Los Angeles, to my current mentor at UCSF, Dr Benjamin Breyer, and many others, I am motivated by the opportunities I have received to advance urologic knowledge and uplift future generations.