Abstract
This Undergraduate Thesis Portfolio brings together two related pieces of work: a
technical capstone project focused on clinical trial endpoint analysis in choroideremia and a
Science, Technology, and Society (STS) research paper examining how patient data is governed
in rare-disease clinical research. While the capstone project focuses on finding meaningful
outcome measures to improve therapeutic evaluation, the STS paper explores the systems and
structures that control access to and use of patient data needed for these analyses. Together, these
projects reflect a broader investigation into how biomedical innovation depends not only on
technological progress but also on ethical, regulatory, and institutional factors. By combining
quantitative analysis with sociotechnical insights, this portfolio highlights the close link between
data-driven decision-making and the governance systems that support it.
The capstone project, titled "Finding Endpoints that Matter for Choroideremia Patients,"
explores limitations in current clinical trial endpoints for a rare, progressive retinal disease.
Traditionally, visual acuity has been the main measure used in ophthalmologic trials; however, in
choroideremia, visual acuity often stays stable until later stages, making it less useful for early
assessment of treatment effects. To fill this gap, the project analyzed natural history data from
the NIGHT study and compared several functional and structural endpoints, including
microperimetry, contrast sensitivity, and imaging-based measures. An age-stratified approach
(≤40 vs. >40 years) was used to capture differences in disease progression across patient groups.
The analysis suggested that some endpoints, especially contrast sensitivity, could be more
sensitive indicators of early functional decline than visual acuity. These insights are particularly
relevant, given previous gene therapy trials where treatments were safe but showed limited
improvement on traditional endpoints. Overall, the capstone aims to support better trial designs
and help develop more effective therapies for rare retinal diseases.
The STS research paper, titled "Patient Data Governance in Rare Disease Clinical Trials:
A Political Technologies Analysis," looks at how patient data is managed, shared, and restricted
in rare disease research. Using a political technologies framework, the paper examines
mechanisms like data use agreements, access committees, de-identification protocols, and
regulations that shape how researchers access and use clinical data. The analysis shows that
while these governance structures are meant to protect patient privacy and promote ethical use,
they can also create barriers to scientific progress, especially since data is already limited in rare
diseases. Through case studies and policy review, the paper highlights conflicts between data
protection and data accessibility and discusses emerging models like dynamic consent and data
trusts as possible solutions. Ultimately, the STS work emphasizes that technical research can’t be
separated from the systems that control data flow, as these systems directly influence what
research can be done.
Working on both projects at the same time gave me a deeper understanding of how
technical analysis and sociotechnical systems are connected. The capstone required access to
detailed patient data to evaluate disease progression and find meaningful endpoints, while the
STS paper revealed the complex ethical and regulatory considerations involved in accessing that
data. This experience made clear that limitations in technical research are often shaped by
broader institutional constraints, not just methodology. Engaging with both the analytical and
governance aspects of clinical research strengthened my ability to approach biomedical
challenges in a more holistic way. It also underscored the importance of designing systems that
respect patient privacy while fostering data-driven innovation. Without this balanced perspective,
it would be easy to overlook how governance structures impact scientific progress. These projects
show that advancing healthcare isn’t just about better models and analyses but about thoughtfully
considering the systems that support them.
