Disease-area comparison
Clinical trial failures by disease area
Compare stopped-trial volume, efficacy/futility signals, safety signals, and operational stops across therapeutic areas in one source-linked table.
Terminated, withdrawn, and suspended records.
Efficacy/futility and safety classifications.
Distinct rows in the comparison below.
ClinicalTrials.gov API v2
Current dataset
Stopped-trial evidence by disease area
Disease areas are assigned through the site's condition and MeSH-term mapping. The biological-signal share helps separate scientific stop language from raw stopped-trial volume.
| Disease area | Total stopped | Likely biological | Efficacy / futility | Safety | Operational |
|---|---|---|---|---|---|
| OncologyKeyword-mapped disease area | 7,941 | 5827% of stopped | 305 | 277 | 4,217 |
| OtherKeyword-mapped disease area | 5,788 | 3887% of stopped | 259 | 129 | 2,827 |
| Infectious DiseaseKeyword-mapped disease area | 1,709 | 1458% of stopped | 87 | 58 | 834 |
| Gastroenterology & HepatologyKeyword-mapped disease area | 1,529 | 1399% of stopped | 87 | 52 | 791 |
| CardiovascularKeyword-mapped disease area | 1,292 | 867% of stopped | 52 | 34 | 728 |
| NeurologyKeyword-mapped disease area | 1,003 | 11912% of stopped | 92 | 27 | 497 |
| Endocrine & MetabolicKeyword-mapped disease area | 799 | 597% of stopped | 28 | 31 | 397 |
| Immunology & AutoimmuneKeyword-mapped disease area | 669 | 6710% of stopped | 41 | 26 | 334 |
| RespiratoryKeyword-mapped disease area | 629 | 6110% of stopped | 37 | 24 | 308 |
| Psychiatry & Mental HealthKeyword-mapped disease area | 609 | 346% of stopped | 23 | 11 | 343 |
| Renal & UrologyKeyword-mapped disease area | 479 | 347% of stopped | 24 | 10 | 248 |
| Hematology (non-onc)Keyword-mapped disease area | 338 | 309% of stopped | 11 | 19 | 178 |
| DermatologyKeyword-mapped disease area | 317 | 3210% of stopped | 28 | 4 | 181 |
| OphthalmologyKeyword-mapped disease area | 261 | 3513% of stopped | 22 | 13 | 106 |
| MusculoskeletalKeyword-mapped disease area | 254 | 104% of stopped | 4 | 6 | 130 |
- Biological
- 582 (7%)
- Efficacy
- 305
- Safety
- 277
- Operational
- 4,217
- Biological
- 388 (7%)
- Efficacy
- 259
- Safety
- 129
- Operational
- 2,827
- Biological
- 145 (8%)
- Efficacy
- 87
- Safety
- 58
- Operational
- 834
- Biological
- 139 (9%)
- Efficacy
- 87
- Safety
- 52
- Operational
- 791
- Biological
- 86 (7%)
- Efficacy
- 52
- Safety
- 34
- Operational
- 728
- Biological
- 119 (12%)
- Efficacy
- 92
- Safety
- 27
- Operational
- 497
- Biological
- 59 (7%)
- Efficacy
- 28
- Safety
- 31
- Operational
- 397
- Biological
- 67 (10%)
- Efficacy
- 41
- Safety
- 26
- Operational
- 334
- Biological
- 61 (10%)
- Efficacy
- 37
- Safety
- 24
- Operational
- 308
- Biological
- 34 (6%)
- Efficacy
- 23
- Safety
- 11
- Operational
- 343
- Biological
- 34 (7%)
- Efficacy
- 24
- Safety
- 10
- Operational
- 248
- Biological
- 30 (9%)
- Efficacy
- 11
- Safety
- 19
- Operational
- 178
- Biological
- 32 (10%)
- Efficacy
- 28
- Safety
- 4
- Operational
- 181
- Biological
- 35 (13%)
- Efficacy
- 22
- Safety
- 13
- Operational
- 106
- Biological
- 10 (4%)
- Efficacy
- 4
- Safety
- 6
- Operational
- 130
How to use the data
What this comparison does and does not show
Large fields produce large counts
Oncology has far more stopped records than many therapeutic areas, partly because it contains a large and diverse clinical research pipeline. Absolute counts are useful for finding evidence, but they should not be presented as risk without an appropriate denominator.
Signal share changes the comparison
The likely biological share asks a narrower question: among the stopped records in a disease area, how many carry efficacy/futility or safety language? This still is not an overall trial failure rate, but it is more informative than volume alone.
Taxonomy is analytical
Disease-area assignment is based on condition and MeSH-term mappings, so complex or cross-specialty studies may fit more than one clinical interpretation. The displayed category is a practical research grouping rather than a medical ontology claim.
Method and limitations
Read the classification as a screening signal
Each trial is counted in its published disease-area category in the compact dataset. The comparison includes all areas rather than only the largest categories.
Efficacy/futility, safety, and operational counts come from the same classifier used throughout the database. Other/unknown and regulatory records remain part of each area's total.
Open an area hub to review individual NCT records. For high-stakes analysis, verify the condition, intervention, phase, and stop statement at ClinicalTrials.gov.
How classification works →Source-linked examples
Records behind the comparison
Examples are selected from the current ingest and link to the corresponding NCT evidence page.
PDE4 Inhibition in Seborrheic Dermatitis and Papulopustular Rosacea
Icahn School of Medicine at Mount Sinai
The study ended early after interim analysis.
Open source-linked record →Study of the Safety, Tolerability and Efficacy of NP-101 in Treating High Risk Participants Who Are Covid-19 Positive.
Novatek Pharmaceuticals
The interim analysis results met protocol and SAP predetermined criteria for stopping early due to futility or efficacy.
Open source-linked record →A Safety and Antitumor Activity Trial of Immunoradiotherapy Combinations as a Treatment Option for Subjects With Metastatic Solid Tumors
Genmab
After reviewing all available data from Part 1 of the trial, the Sponsor has decided not to proceed to Part 2 (randomized Phase 2) of the trial due to lack of efficacy
Open source-linked record →Phase III, Efficacy and Safety of "Kamada-AAT for Inhalation"
Kamada, Ltd.
Following interim analysis for futility
Open source-linked record →Low-dose Droperidol for Prevention of Postoperative Delirium in Elderly Patients After Non-cardiac Surgery
RenJi Hospital
The trial was terminated on the independent DSMB's recommendation after an interim analysis showed no evidence of benefit and a numerically higher incidence of postoperative delirium in the droperidol group,...
Open source-linked record →A Study to Investigate Safety, Tolerability, Pharmacokinetics (PK), Pharmacodynamics (PD), and Immunogenicity of RO7669330 in Participants With Geographic Atrophy (GA) Secondary to Age-related Macular Degeneration (AMD)
Hoffmann-La Roche
The study was terminated prematurely by the Sponsor due to observation of dose-limiting adverse events meeting the protocol-defined stopping criteria.
Open source-linked record →FAQ
Questions about this comparison
Which disease area has the most stopped clinical trials?
Oncology is the largest disease-area slice in the current dataset. This reflects stopped-record volume, not the probability that an oncology trial fails.
Is biological-signal share the same as clinical trial failure rate?
No. It is the share of stopped records classified as efficacy/futility or safety. A true failure rate requires all eligible trials as the denominator.
How are disease areas assigned?
The pipeline maps registry conditions and MeSH terms into practical disease-area groups. Important records should still be checked at the source.