Federal judge invalidates patents – but on the basis of a common statistical mistake

In a recent case before the US District Court in the District of Nevada, the judge ruled six method-of-use patents to be invalid because the claims were obvious based on previously published data (“prior art”). Curfman et al. (2020) argue her decision was incorrect, because the judge interpreted the published statistical analysis incorrectly.  

Mori et al. (2000) measured LDL cholesterol before and after giving participants (17-19 per group) docosahexaenoic acid (DHA) or eicosapentaenoic acid (EPA). DHA increased LDL cholesterol by 8.0% (P= 0.019) while EPA increased LDL by 3.5% (p>0.05) as compared to vehicle (olive oil). The judge said these findings clearly demonstrated that DHA and EPA have different effects. It’s “obvious” (which has special meaning in patent cases) because the effect of DHA was statistically significant, while the effect of EPA was not.

Curfman et al. disagree with this conclusion because the study did not compare EPA to DHA, but only each of them to vehicle. They performed a t test comparing the effects (change pre vs post treatment) of EPA and DHA, showed that the p-value must be high, and so concluded that the null hypothesis that the two compounds have equal effects on LDL cannot be rejected. They didn’t report the p-value, but we calculate it to be 0.33 (without correcting for multiple comparisons). This example demonstrates that the difference between ‘significant’ and ‘non-significant’ may itself not be statistically significant (Gelman and Stern, 2006).

Curfman et al. argue that conclusions based on flawed statistical analysis should not be accepted as prior art. The case has been accepted by the US Court of Appeals for the Federal Circuit and could have a major effect on the future of the biotechnology industry. In a broader sense this case brings up the question whether flawed data/inaccurate science can be used to support patents and further highlights the need for sufficient training of scientists to enable a sound understanding of statistical analysis.

Share this

Leave a Reply

Your email address will not be published. Required fields are marked *