Contraview Research
Measuring what actually predicts success in biotech
A knowledge graph of companies, programmes, trials and capital, and a testing discipline built to tell real signal from noise.
24,000+companies
76,000clinical trials
448,000graph entities
2,800variables tracked
Selected signals
Private companies · outcome within 36 months · 14,208 company-years
| Signal | Outcome | Effect | 95% CI | p |
|---|---|---|---|---|
| Specialist biotech fund on the cap table | Any exit | 2.0x | 1.8 to 2.3 | <0.001 |
| Two or more specialist funds | Takeout of $100M+ | 2.9x | 2.1 to 3.9 | <0.001 |
| Pharma investor on the cap table | Takeout of $100M+ | 2.2x | 1.5 to 3.0 | <0.001 |
| Trial stopped in the prior 3 years | Any acquisition | 0.37x | 0.16 to 0.61 | 0.004 |
Risk ratios against companies without the signal, 2017 to 2022, adjusted for year and data depth. Intervals from a company-clustered bootstrap; p values approximated from the intervals. Associations, not causes.
Every variable is audited
1,396 variables tested · 62% pass
863 usable255 leak145 circular123 model output10 coverage proxy
Leak: the variable encodes the outcome or was recorded after it. Circular: it is built from the outcome. Model output: another model's score, not a measurement.
Contraview Research
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