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<span class="chip chip-accent">DS4CABS · 2026 Summer Intern</span>

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<div>
<h1 class="profile-name">Reuben N. Addison, Ph.D.</h1>
<p class="profile-title">Clinical Research Scientist · Quantitative Researcher</p>
<p class="profile-org">DePauw University · DS4CABS 2026 Summer Intern</p>

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<a class="btn btn-primary" href="https://www.linkedin.com/in/rnaddison/" target="_blank" rel="noopener">LinkedIn ↗</a>
<a class="btn btn-ghost" href="https://github.com/reubebe" target="_blank" rel="noopener">GitHub ↗</a>
<a class="btn btn-ghost" href="https://github.com/ds4cabs/OpenTrial" target="_blank" rel="noopener">OpenTrial ↗</a>
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<h3>About</h3>
<p>
Reuben Addison is a clinical research scientist and quantitative researcher working at the
intersection of study design, Bayesian statistics, and biomedical data science. His work
focuses on making the assumptions behind scientific and clinical decisions explicit,
traceable, and testable.
</p>
<p>
His research spans Parkinson's disease, longitudinal clinical outcomes, cardiovascular
risk, movement science, and clinical trial design. He is an Assistant Professor of
Kinesiology and Director of the Motor Control Lab at DePauw University, holds a Ph.D. in
Motor Behavior, and completed the Computational Data Analytics Track, the data science
track, of Georgia Tech's M.S. in Analytics.
</p>
</div>

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<h3>CABS Internship Project</h3>
<p>
During the 2026 CABS Data Science Summer Internship, Reuben developed
<strong>OpenTrial</strong>, a Bayesian clinical trial design engine under the mentorship
of Prof. Shicheng Guo.
</p>
<p>
OpenTrial starts with a treatment and endpoint, gathers relevant public evidence, and
produces a traceable study-design report. It integrates sources including
ClinicalTrials.gov, PubMed, openFDA, DailyMed, Open Targets, PharmGKB, and Semantic
Scholar, then connects that evidence to power, assurance, prior-sensitivity analysis,
predictive probability of success, and simulation-based design calculations.
</p>
<p>
The central rule is simple: only evidence with a quantifiable effect size and associated
standard error is allowed to update the prior. Other records remain visible for provenance,
but they do not quietly influence the mathematics.
</p>
</div>

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<h3>Validation and Sensitivity Analysis</h3>
<p>
Reuben treated validation as part of the research problem, not as a final software check.
OpenTrial includes 100 offline tests and 46 independent validation checks against SciPy,
statsmodels, closed-form calculations, and separately written Monte Carlo implementations.
</p>
<p>
That process exposed implementation problems that ordinary unit testing had missed,
including a Type I error calculation that appeared correct because calibration and
evaluation had reused the same simulated paths, and a heterogeneity calculation that
double-counted sampling error. Both were corrected.
</p>
<div class="result-card">
<strong>Type I error:</strong> 0.0234 against a 0.025 target.
</div>
<div class="result-card">
<strong>Prior sensitivity:</strong> in a seeded Type 2 diabetes example, the same
80-patient-per-arm design produced an estimated probability of success of 0.87 under the
evidence-derived prior and 0.18 under a skeptical prior. The design did not change. The
assumption did.
</div>
</div>

<div class="profile-section">
<h3>Areas of Focus</h3>
<ul class="pill-list">
<li>Clinical trial design</li>
<li>Bayesian statistics</li>
<li>Biostatistics</li>
<li>Longitudinal modeling</li>
<li>Real-world evidence</li>
<li>Biomedical machine learning</li>
<li>Parkinson's disease</li>
<li>Movement science</li>
<li>Reproducible research</li>
<li>Evidence provenance</li>
</ul>
</div>

<div class="profile-section">
<h3>What's Next</h3>
<p>
Reuben is continuing to work at the intersection of clinical research, Bayesian trial
design, biomedical machine learning, and reproducible evidence pipelines through his lab
at DePauw and collaborations in biopharma.
</p>
<p>
He is particularly interested in problems where quantitative methods can make a clinical
or development decision more transparent, reproducible, and easier to defend. OpenTrial
remains a learning-oriented research tool and is not validated for real-world clinical use.
</p>
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