01
The protocol is harder to recruit than it looks.
Narrow diagnosis windows, strict screening thresholds, and demanding visit schedules shrink the real patient pool well below the headline population.
Clinical Trial Analysis
Independent analysis of clinical protocols, site feasibility, and study economics—with transparent evidence and assumptions.
Protocol Review
Feasibility & Sites
Budgeting & Economics
Where Planning Goes Wrong
01
Narrow diagnosis windows, strict screening thresholds, and demanding visit schedules shrink the real patient pool well below the headline population.
02
Overlapping line items, unpriced components, and flat monthly estimates can hide what actually drives cost until a vendor or site proposal arrives.
03
A registry listing or a strong reputation doesn't show eligible-patient volume, competing trials, or whether a site has the capacity to deliver.
We make those assumptions explicit, quantify them, and show which ones to verify first.
Book a scoping callServices
Study-specific analysis, delivered as actionable reports with a walkthrough.
Review of study design, eligibility criteria, and endpoints to identify recruitment and analysis risks.
Identify protocol risks early.
Patient-pool estimates, competing-trial analysis, and evidence-based site evaluation.
Establish a defensible enrollment range.
Scenario-based study budgets, cost reconciliation, and review of CRO and site proposals.
Build a budget you can defend and negotiate.
How It Works
01
Define the study, key decisions, scope, and required materials.
02
Evaluate protocols, sites, enrollment, and costs using documented evidence and explicit assumptions.
03
Receive a client-ready report, walkthrough, and prioritized next steps.
Every figure is classified by its supporting evidence, distinguishing verified facts from estimates and assumptions.
About
Founder & CEO, Clinovia.ai
Adjunct Assistant Professor
The University of Texas at Dallas
I'm a quantitative scientist who helps clinical development teams evaluate their plans before they commit to them. My training is in mathematics and engineering, with a PhD in electrical and computer engineering focused on quantum machine learning. I also serve as an Adjunct Assistant Professor at UT Dallas, where my academic work includes healthcare AI.
Developing machine-learning models to predict progression from mild cognitive impairment to Alzheimer's disease gave me practical experience working with clinical outcomes, patient characteristics, longitudinal data, and predictive uncertainty. I bring that analytical discipline to clinical protocol review, feasibility assessment, and study economics. My work connects evidence, assumptions, and quantitative analysis to the decisions sponsors need to make.
Where the data and intended use justify it, I also develop predictive models for trial enrichment and prescreening. Across all engagements, I emphasize transparent assumptions, traceable evidence, and analysis that is useful for real-world decisions.
Our analysis supports your team's decisions. It does not replace protocol, biostatistical, medical, or regulatory review.
Book a scoping callContact
A scoping call is 30 minutes. We'll discuss your study, the question behind it, and whether an analysis would help. There's no obligation.
Prefer email? hello@clinovia.com