Mahmood Ahmad
Tahir Heart Institute
author@example.com

CT.gov Country-Condition Hiddenness

Which disease-country cells look quietest on ClinicalTrials.gov once older closed interventional studies are split simultaneously by condition family and named study location? We analysed 249,507 eligible older closed interventional studies from the March 29, 2026 full-registry snapshot and exploded named-country involvement within selected condition families. The project compares two-year no-results rates, ghost-protocol rates, and visible shares for oncology, cardiovascular, and metabolic studies across country-condition cells with at least 400 studies. Oncology studies involving China reached 79.0 percent no results versus 52.6 percent for oncology studies involving the United States. Cardiovascular studies involving Egypt reached 95.9 percent no results, while metabolic studies involving China reached 78.9 percent and Denmark 79.6 percent. Disease and geography therefore interact rather than add independently, because the same condition family looks materially different once specific country footprints are named inside the same nominal therapeutic area. Country-condition cells reflect recorded study locations rather than country-specific enrollment shares, sponsor domicile, or national reporting mandates.

Outside Notes

Type: methods
Primary estimand: 2-year no-results rate across selected country-by-condition cells among eligible older CT.gov studies
App: CT.gov Country-Condition Hiddenness dashboard
Data: 249,507 eligible older closed interventional studies exploded into named-country condition-family cells
Code: https://github.com/mahmood726-cyber/ctgov-country-condition-hiddenness
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.