Clinical Development
Size the Study Before You Commit to It
Find out whether the patients for your study exist, and what each criterion costs you before a single site is chosen.
Why the Population Number Holds
Patient journeys in the Healthcare Map carry an average of seven years of history.
Clinical Development Analytics
From first feasibility read to enrollment, clinical development teams use Komodo for clinical trial analytics: sizing the eligible population and finding the sites that can reach it.
Trial Feasibility
Size the eligible population and see how often a candidate endpoint event occurs before the design is final.
Site Selection
Rank sites on observed patient volume and check each against its enrollment projection, so shortfalls surface at planning, not at the milestone.
Eligibility Criteria
Test inclusion and exclusion criteria against de-identified patient records to see how each one moves the eligible pool.
External Control Arms
Support a comparator arm with de-identified patient records, so a single-arm study still has something to measure against.
Enrollment Diversity
See the demographic and geographic makeup of the eligible population, and where your site list misses part of it.
Treatment Pathways
Map how patients move through diagnosis, treatment, and switching, so protocol design reflects the care patients actually receive.
Built for Decisions You Can’t Undo
Clinical decisions are hard to reverse. Regulators, reviewers, and partners question them, sometimes years later. That sets a higher bar for the analytics behind them.
Before the Protocol Locks
Find the Flaw While It’s Still Cheap
Test an assumption against patients who actually received care, with an average of seven years of history behind each journey in the Healthcare Map. The codes and cohorts are already mapped, so the answer arrives while the protocol is still a draft.
A Clinical Development Platform Methods That Survive a Second Reader
With Marmot, clinical science teams can generate the SQL behind any cohort and run it independently. Marmot checks cohort sizes and code sets against clinical coding standards and benchmarks before results reach the team.
Built for a Long Timeline
The Cohort Outlives the Study
Cohort definitions, criteria, and analyses are validated once and saved to a shared Library. The eligibility logic behind a Phase II study is still readable at readout, at publication, and when someone asks whether the same population supports a second indication.
“Marmot has helped us achieve substantial speed. It used to take us six months to do studies, and now, using Marmot, the shortest study we’ve done is over a weekend.”
Christopher Blanchette, PhD, MBA, VP of Clinical Data Science & Evidence·Novo Nordisk
Clinical Development FAQs
How can analytics improve clinical trial site selection?
Site lists built on past trial participation tend to repeat past enrollment. Clinical trial site selection analytics ranks sites using de-identified real-world patient data, so site feasibility reflects the patients each site treats now rather than who it enrolled before. That shows where a list misses part of the eligible population.
What is an external control arm, and when is one used?
An external control arm uses de-identified real-world patient records in place of a randomized control group, typically where randomization is not feasible or ethical: rare disease, pediatric populations, single-arm oncology studies. Any external control needs records deep enough to follow the comparator population’s outcomes over time.
Is Komodo a clinical trial management system (CTMS)?
No. A CTMS or EDC system runs a trial you have already designed: sites, visits, case report forms, documents. Komodo works upstream of that on the decisions that set the trial’s shape: which population is eligible, which criteria narrow it, and which sites treat those patients. The two sit at different points in the timeline.
Does Komodo cover patients outside the U.S.?
The Healthcare Map covers the United States. For a global program, it supports U.S. population sizing, U.S. site selection, and U.S. comparator cohorts, and clinical teams typically pair it with other sources for ex-U.S. planning.
How does this work alongside our CRO?
Komodo does not replace the CRO. It gives you a patient-level view of the eligible population and the sites treating them, so CRO site recommendations can be evaluated against observed treatment volume rather than accepted on estimates alone.
Every Protocol Carries an Untested Assumption
Bring the one you’d rather not be wrong about. Test it against de-identified patient data while there’s still time to change it.