Dataset

glp1-clinical-trials-2026

A CC-BY-4.0 licensed compilation from Ozari Health cataloging 22 major published GLP-1 clinical trials as of May 2026, consolidating peer-reviewed trial-level data into a single structured index.

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The GLP-1 Clinical Trials Index, published by Ozari Health in 2026, is a structured CSV compilation of peer-reviewed trial results for major GLP-1 receptor agonists. It consolidates endpoint, responder, and population data across semaglutide, tirzepatide, liraglutide, oral semaglutide, and orforglipron into a single comparable schema.

What this dataset contains

The dataset covers published clinical trials for leading GLP-1 therapies in weight management, diabetes, cardiovascular outcomes, renal outcomes, and sleep apnea. Each row represents one trial, with drug identity, dose, mechanism, trial_phase, and the citation traceable through the doi and journal fields. Data is verified against label information and, where responder rates are not in the primary paper, sourced from supplementary materials.

Structure and fields

The CSV contains 22 rows and 25 columns. Core efficacy fields include n_participants, duration_weeks, primary_endpoint, mean_tbwl_percent, mean_tbwl_placebo_percent, mean_weight_loss_lbs, and tiered responder thresholds responders_5pct, responders_10pct, responders_15pct, and responders_20pct, alongside a1c_reduction_percent and key_secondary_finding. Provenance is captured in publication_year, journal, doi, trial_sponsor, population, and free-text notes, with trial_name, drug, drug_brand_name, dose, and mechanism providing identification.

How analysts use it

Healthcare and pharma analysts can benchmark efficacy across molecules by filtering on drug, dose, and trial_phase, comparing mean_tbwl_percent against mean_tbwl_placebo_percent and the responder columns. The structured citation layer, anchored by doi and journal, supports due-diligence checks and competitive landscape mapping for GLP-1 sponsors. Given the small row count, the dataset functions best as a reference index for headline efficacy comparisons rather than statistical modeling.

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