Mahmood Ahmad
Tahir Heart Institute
author@example.com

CT.gov U.S. Versus Ex-U.S. Sponsor Classes

How different do sponsor-class reporting gaps look once older CT.gov studies are collapsed into any-U.S., no-U.S., and no-country buckets instead of a longer geography table? We analysed 249,507 eligible older closed interventional studies from the March 29, 2026 full-registry snapshot and grouped them by U.S. presence using recorded country locations. The project compares two-year no-results rates, ghost-protocol rates, visible shares, and sponsor-class contrasts across any-U.S., no-U.S., and no-country portfolios. Any-U.S. studies showed a 52.1 percent no-results rate, versus 88.7 percent for no-U.S. studies and 80.9 percent for studies with no named country. Within no-U.S. studies, OTHER reached 94.9 percent no results and industry 70.9 percent, while any-U.S. industry fell to 45.5 percent and any-U.S. NIH to 52.3 percent. U.S. presence therefore behaves like a divider across sponsor classes, and the ex-U.S. backlog is much quieter than the any-U.S. registry surface. U.S.-presence buckets reflect recorded study locations rather than verified enrollment shares, sponsor domicile, or legal obligations.

Outside Notes

Type: methods
Primary estimand: 2-year no-results rate across sponsor classes within any-U.S., no-U.S., and no-country older CT.gov portfolios
App: CT.gov U.S. Versus Ex-U.S. Sponsor Classes dashboard
Data: 249,507 eligible older closed interventional studies grouped into any-U.S., no-U.S., and no-country buckets
Code: https://github.com/mahmood726-cyber/ctgov-us-vs-exus-sponsor-classes
Version: 1.0.0
Validation: FULL REGISTRY RUN

References

1. ClinicalTrials.gov API v2. National Library of Medicine. Accessed March 29, 2026.
2. Zarin DA, Tse T, Williams RJ, Carr S. Trial reporting in ClinicalTrials.gov. N Engl J Med. 2016;375(20):1998-2004.
3. DeVito NJ, Bacon S, Goldacre B. Compliance with legal requirement to report clinical trial results on ClinicalTrials.gov: a cohort study. Lancet. 2020;395(10221):361-369.

AI Disclosure

This work represents a compiler-generated evidence micro-publication built from structured registry data and deterministic summary code. AI was used as a constrained coding and drafting assistant for interface generation, packaging, and prose refinement, not as an autonomous author. The analytical choices, interpretation, and final outputs were reviewed by the author, who takes responsibility for the content.