Author: Jeremy Berg

After reading yesterday’s post, a Feedback Loop reader asked for a full regression analysis of the overall impact score based on all five criterion scores. With the caveat that one should be cautious in over-interpreting such analyses, here it is:

Pearson correlation coefficients of overall impact score and five criterion scores in a sample of 360 NIGMS R01 applications reviewed during the October 2009 Council round.

As one might expect, the various parameters are substantially correlated.

A principal component analysis reveals that a single principal component accounts for 71% of the variance in the overall impact scores. This principal component includes substantial contributions from all five criterion scores, with weights of 0.57 for approach, 0.48 for innovation, 0.44 for significance, 0.36 for investigator and 0.35 for environment.

Here are more results of the full principal component analysis:

Principal component analysis of overall impact score based on the five criterion scores in a sample of 360 NIGMS R01 applications reviewed during the October 2009 Council round.

The second component accounts for an additional 9% of the variance and has a substantial contribution from approach, with significant contributions of the opposite sign for investigator and environment. The third component accounts for an additional 8% of the variance and appears to be primarily related to innovation. The fourth component accounts for an additional 7% of the variance and is primarily related to significance. The final component accounts for the remaining 5% of the variance and has contributions from investigator and environment of the opposite sign.

In an earlier post, I presented an analysis of the relationship between the average significance criterion scores provided independently by individual reviewers and the overall impact scores determined at the end of the study section discussion for a sample of 360 NIGMS R01 grant applications reviewed during the October 2009 Council round. Based on the interest in this analysis reflected here and on other blogs, including DrugMonkey and Medical Writing, Editing & Grantsmanship , I want to provide some additional aspects of this analysis.

As I noted in the recent post, the criterion score most strongly correlated (0.74) with the overall impact score is approach. Here is a plot showing this correlation:

Plot of approach and overall impact scores in a sample of 360 NIGMS R01 applications reviewed during the October 2009 Council round.

Similarly, here is a plot comparing the average innovation criterion score and the overall impact score:

Plot of innovation and overall impact scores in a sample of 360 NIGMS R01 applications reviewed during the October 2009 Council round.

Note that the overall impact score is NOT derived by combining the individual criterion scores. This policy is based on several considerations, including:

• The effect of the individual criterion scores on the overall impact score is expected to depend on the nature of the project. For example, an application directed toward developing a community resource may not be highly innovative; indeed, a high level of innovation may be undesirable in this context. Nonetheless, such a project may receive a high overall impact score if the approach and significance are strong.
• The overall impact score is refined over the course of a study section discussion, whereas the individual criterion scores are not.

That being said, it is still possible to derive the average behavior of the study sections involved in reviewing these applications from their scores. The correlation coefficient for the linear combination of individual criterion scores with weighting factors optimized (approximately related to the correlation coefficients between the individual criterion scores and the overall impact factor) is 0.78.

The availability of individual criterion scores provides useful data for analyzing study section behavior. In addition, these criterion scores are important parameters that can assist program staff in making funding recommendations.