Sponsored-Research Funding by Newly Recruited Assistant Professors: Can It Be Modeled as a Sequential Series of Uncertain Events?

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Abstract

Purpose.

Recruitment of junior faculty with an investigative focus is essential to regenerate and expand the research mission of academic health centers. Predicting funding profiles for junior faculty is limited by variability in the timing, magnitude, and duration of projected research grant funding. The author demonstrated the validity of Monte Carlo simulation to predict sponsored-research revenues by newly recruited faculty.

Method.

Demographic characteristics and funding profiles were determined for assistant professors recruited to Yale University School of Medicine in four separate fiscal years (1992–93, 1993–94, 1996–97, 1997–98). These data were applied to develop and assess the simulation model.

Results.

Only when assistant professors were subcategorized by type of research was it possible to accurately predict recovery of both direct research costs and facilities and administrative costs. Simulations illustrated both the high degree of variability among individual faculty and also the advantage of a prediction tool that displays the range and probability of all possible outcomes.

Conclusion.

Sponsored-research funding by newly recruited assistant professors can be modeled as a sequential series of uncertain events and used to predict consequences of imminent changes in federal funding for biomedical research.

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