Mahmood Ahmad
Tahir Heart Institute
author@example.com

CT.gov Completion-Delay Debt

Does ClinicalTrials.gov hiddenness fall as trials take longer from first submission to completion, or do short-cycle studies report just as well? We analysed 249,507 eligible older closed interventional studies from the March 29, 2026 full-registry snapshot and calculated submission-to-completion delay buckets. The project compares two-year no-results rates, ghost-protocol rates, full visibility, and purpose-specific contrasts across registration-to-completion intervals. Studies completed in the same calendar year they were first submitted showed an 85.7 percent no-results rate and a 54.1 percent ghost-protocol rate. Studies with a 6 to 10 year delay fell to 57.6 percent no results and 28.8 percent ghost protocols, with long-lag treatment studies also looking substantially cleaner. Fast-cycle studies therefore look most hidden, suggesting short operational timelines are not translating into faster public reporting. The contrast remains visible across treatment studies and other major purpose groups. Submission-to-completion lag is a registry proxy for operational duration and can reflect backfilled dates, protocol amendments, or changing trial mix.

Outside Notes

Type: methods
Primary estimand: 2-year no-results rate across registration-to-completion delay buckets among eligible older CT.gov studies
App: CT.gov Completion-Delay Debt dashboard
Data: 249,507 eligible older closed interventional studies grouped by submission-to-completion delay
Code: https://github.com/mahmood726-cyber/ctgov-completion-delay-debt
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.