Clinical Trial Completion Probability Dataset
The Clinical Trial Success Probability Dataset predicts trial completion to help organisations assess risk and benchmark performance across therapeutic areas.
Overview
The Clinical Trial Success Probability Dataset provides forward-looking probabilities of completion for active interventional clinical trials. Built on a decade of historical outcomes from ClinicalTrials.gov, it transforms complex registry records into decision-ready insights that help organisations evaluate risk, allocate resources, and benchmark performance across therapeutic areas.
Insights Delivered
This dataset goes beyond raw trial listings by extracting the signals that matter most:
Probability of completion – A calibrated score (0–1) with High / Medium / Low risk tiers for rapid triage.
Eligibility complexity – Measures of how restrictive and readable inclusion/exclusion criteria are, key drivers of recruitment success.
Therapeutic area context – TA buckets and condition flags to benchmark probabilities within comparable areas.
Design signals – Randomisation and masking/blinding context that highlight trial-level rigour.
How to Use
Rank & triage – Sort trials by probability and risk tier to focus diligence.
Compare cohorts – Filter by phase or therapeutic area, and benchmark sponsors with the reliability index.
Check feasibility – Combine site counts, geography, and per-site enrolment pressure to flag early execution risks.
Audit criteria – Use complexity scores to identify trials likely to face enrolment challenges.
Use Cases
Investors & Analysts
Weight forecasts by trial completion probability.
Prioritise due diligence on value-driving programs.
Benchmark portfolios against sector averages.
Pharma & CROs
Benchmark ongoing programs against historical outcomes.
Flag high-risk designs (e.g., restrictive eligibility, underpowered multicountry trials).
Guide licensing and partnership prioritisation with funding and sponsor signals.
Consultancies & Advisory Firms
Provide quantified risk assessments of client pipelines.
Benchmark against peer companies by therapeutic area.
Accelerate M&A and due diligence deliverables with ready-to-use data.
Insurers & Risk Managers
Build actuarial models informed by trial success probabilities.
Improve pricing of insurance and coverage products.
Monitor therapeutic areas with historically high termination rates.
Why It Matters
Completion risk is measurable. By combining machine learning with historical trial outcomes, this dataset converts raw registry data into actionable intelligence for finance, pharma, research, and risk management. Predictions are statistical estimates and should always be complemented with expert review.
Model Performance
Predicting clinical trial completion with high accuracy — turning registry data into reliable, decision‑ready insight.
Test Results
Accuracy: 0.875 — overall correctness
Precision: 0.859 — when predicting completion, correct 85.9% of the time
Recall: 0.990 — captures nearly all completed trials
F1 Score: 0.919 — balanced precision and recall
ROC‑AUC: 0.865 — strong discrimination between outcomes
Why it matters
These results mean users can rely on the dataset not just for raw predictions, but for calibrated, decision-ready probabilities — essential for investment models, trial portfolio benchmarking, and risk management.
Disclaimer
The Clinical Trial Success Probability Dataset is provided for informational and research purposes only. While the dataset applies machine learning and statistical methods to historical records, no guarantee is made as to the accuracy, completeness, or timeliness of the information.
This dataset does not constitute financial, investment, medical, or legal advice, and it should not be relied upon as the sole basis for decision-making. Users are responsible for conducting their own due diligence and obtaining appropriate professional advice before making strategic, financial, or healthcare-related decisions.
Data Fusion accepts no liability for any losses, actions, claims, proceedings, demands, costs, expenses, damages, or other liabilities arising from use of the dataset or reliance on its outputs.
Attribution footer
Data derived from ClinicalTrials.gov, a resource of the U.S. National Library of Medicine. Not affiliated with or endorsed by ClinicalTrials.gov.
