Mahmood Ahmad
Tahir Heart Institute
author@example.com

CT.gov Sponsor Primary-Outcome Gap

Which named sponsors most often leave older CT.gov study pages without primary outcome descriptions, obscuring what the main endpoint was meant to measure? We analysed 249,507 eligible older closed interventional studies from the March 29, 2026 full-registry snapshot and ranked sponsors with at least 100 studies. We defined a primary-outcome gap as a missing primary outcome description, then compared sponsor stock, rate, and sponsor-class patterns. GlaxoSmithKline led the sponsor primary-outcome-gap stock table at 820 studies, followed by Boehringer Ingelheim at 759, Sanofi at 749, and Pfizer at 645. Ranbaxy Laboratories Limited had the sharpest large-sponsor primary-outcome-gap rate at 98.0 percent, while Mylan Pharmaceuticals reached 94.3 percent and NIH reached 30.7 percent as a sponsor class. Missing the primary outcome description removes the line telling readers what the main endpoint was, even when other registry fields remain visible. These gaps describe missing registry fields and do not by themselves establish legal non-compliance or missing outcome data elsewhere.

Outside Notes

Type: methods
Primary estimand: Primary-outcome-gap stock among older studies missing the primary outcome description field
App: CT.gov Sponsor Primary-Outcome Gap dashboard
Data: 249,507 eligible older closed interventional studies with sponsor primary-outcome-gap stock and rate summaries
Code: https://github.com/mahmood726-cyber/ctgov-sponsor-primary-outcome-gap
Version: 1.0.0
Validation: FULL REGISTRY RUN

References

1. ClinicalTrials.gov API v2. National Library of Medicine. Accessed March 29, 2026.
2. 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.
3. Zarin DA, Tse T, Williams RJ, Carr S. Trial reporting in ClinicalTrials.gov. N Engl J Med. 2016;375(20):1998-2004.

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.