---
language:
- en
license: other
extra_gated_prompt: "To get access to this dataset, you must subscribe to Papers With Backtest. To subscribe, go to https://paperswithbacktest.com/ > Login > Choose Your Plan > Subscribe."
dataset_info:
  features:
  - name: symbol
    dtype: string
  - name: datetime
    dtype: string
  - name: success_prediction
    dtype: float64
  - name: economic_effect
    dtype: float64
  - name: duration_prediction
    dtype: float64
  - name: success_composite
    dtype: float64
  - name: class
    dtype: string

---
# Dataset Information

Weekly **clinical trial outcome predictions** for global pharmaceutical sponsors, provided by **SOV.AI**. Each row represents the
latest modelled view for a trial on a given week, combining regulatory success probabilities, time-to-completion expectations, and
an economic impact index.

- **Coverage**: Pharmaceutical and biotech trials with mapped corporate tickers (where available)
- **Update cadence**: Weekly (~1,052 new trials evaluated each week)
- **Performance**: Ensemble models delivering **87% ROC-AUC** on held-out validation data
- **Outputs**: Success probability, composite success score, predicted duration (days), economic effect index, and sponsor class

## Data Source
Data provided by SOV.AI. Access programmatically via:

```python
import sovai as sov
df_clinical = sov.data("clinical/predict", full_history=True)
```

## Notes
- Probabilistic scores (`success_prediction`, `success_composite`) are in the range 0–1.
- `duration_prediction` is expressed in days.
- `economic_effect` is a unitless index indicating the magnitude of expected market response.
- `class` identifies the sponsor type (e.g., INDUSTRY, NIH, OTHER).