R01 grants may be renewed, typically every 4-5 years. These are called "competing renewals" or "Type 2" grants. In the context of discussions of the "Emeritus Award" discussion, I examined the distribution of R01 grants that had been renewed over a long period of time. Here, I look at the distribution of Type 2 grants over the period from 1995 to 2014.
Data for Type 2 R01 grants (as well as the corresponding but much smaller number of R37 MERIT awards) for each year were compiled from NIH RePORTER. Note that these grants only include grants that were competitively renewed in a given year and not non-competing continuations (Type 5 awards). The distributions of the 2653 Type 2 awards from FY1995 and 1532 Type 2 awards from FY2014 over the Support Year are shown below:
The distribution for FY1995 shows peaks at Year 4, Year 6 corresponding to renewals of initial (Type 1) grants of 3 and 5 years, respectively. There are additional peaks at Year 9 and Year 14. These correspond primarily to grants that initially made for a period of 3 years and then were renewed for 5 year periods.
The distribution for FY2014 is similar but shows some important differences. First, the overall amplitude is smaller as there were 58% as many Type 2 R01 grants awarded in FY2014 compared to FY1995. Second, the FY2014 shows peaks at Year 6 and Year 11. These correspond to grants awarded initially for a period of 5 years and then renewed for an additional 5 year period. Third, the FY2014 distribution shows a longer tail extended to Year 40 and beyond, corresponding to long-running grants.
The distributions are shown in normalized and integrated form below:
These curves show more clearly the tail extending longer grant durations. For FY1995, 20% of the grants are in Years 13 or beyond whereas for FY2014, 20% of the grants are in Years 18 and beyond.
The distribution of Type 2 grants can be seen more clearly by looking are the distribution summed over all of the years from FY1995 to FY2014 as shown below.
This shows peaks from Years 5-6 and Years 10-11 and then the tail extending from Year 15 and beyond.
The structure of this tail can be seen more clearly by replotting the data on a log (base 10) scale as shown below:
The portion of this graph corresponding to the extended tail is very nearly linear with a slight downward curvature. This indicates the tail that is approximately exponential. Fitting this curve reveals an exponent of approximately -0.064/year. This corresponds to a half-life 0f 4.7 years. In order words, the chance that a long-standing grant is renewed every 4-5 years is approximately 50%. The NIH reported success rate for all competing renewal Grants averaged 38% from FY2001 to 2014. Thus, it appears that the likelihood that a longstanding R01 is competitively renewed is slightly, but not dramatically, higher than that for R01 grants overall. The slight downward curvature of the log plot likely reflects that fact that there were a smaller number of longstanding grants in the earlier years in this analysis.
An alternative approach to examining trends the duration of these grants involves looking at the fraction of A0 applications among the funded Type 2 grants. I have previously examined this parameter in other contexts. A plot of the fraction of A0 applications among funded Type 2 grants as a function of the Year of the grant is shown below:
This fraction dips slightly for grant years from 4-7 (corresponding to the first renewal) and then reaches a relatively stable level extending out to 40 years. This suggests there is not a major increase in the likelihood of application success as the year of the grant increases.
Overall, these observations are consistent with the notion that R01 grants reach longstanding status through the perseverance of the principal investigators. Over time, these investigators continue to execute research programs that are sufficiently productive that they compete for renewal with a success rate close to 50% (at least historically). There does not appear to be a substantial advantage for longstanding grant applications above the general advantage for Type 2 versus Type 1 applications based on these publicly available data.