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Orthogonal predictions: follow-up questions for suggestive data
1World Health Information Science Consultants, LLC, Newton, MA 02466, USA. Alec.Walker@WHISCON.com
Pharmacoepidemiology and Drug Safety
|March 24, 2010
Summary
Researchers can test biological hypotheses using existing data by looking for novel, orthogonal predictions. This method validates causal inference, distinguishing robust hypotheses from those lacking biological content and predicting only data replication.
Area of Science:
- Biological sciences
- Data science
- Scientific methodology
Background:
- Biological hypotheses of causal effect can arise from study data.
- Testing these hypotheses often requires new datasets.
- Existing methods may struggle to validate hypotheses within the original data.
Purpose of the Study:
- To describe a method for testing biological hypotheses within the original database.
- To differentiate valid hypothesis testing from mere data replication.
- To introduce the concept of 'orthogonal' predictions for hypothesis validation.
Main Methods:
- Inferring biological hypotheses from existing data.
- Identifying 'orthogonal' predictions within the data that are not logical correlates of the hypothesis's origin.
- Assessing the validity of the hypothesis based on these orthogonal predictions.
Main Results:
- A method exists to test biological hypotheses using the originating database.
- Valid hypothesis testing relies on identifying novel, non-correlate predictions.
- 'Scrawny' hypotheses, lacking biological content, fail this validation.
Conclusions:
- Biological hypotheses can be rigorously tested using existing data through orthogonal predictions.
- This approach enhances causal inference and distinguishes meaningful hypotheses.
- The Universal Data Warehouse may streamline this testing process.
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