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Simulation Model for First-Time NIH Principal Investigators: Insights and Future Directions

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This document presents a comprehensive simulation model designed to analyze the demographic shifts within Principal Investigators (PIs) entering NIH grants, focusing specifically on first-time PIs. Utilizing SimBLOX and SimBRIX methodologies, the model illustrates age group transitions, funding gaps, and response dynamics among new investigators. Historical data from FY1980 to FY2005 demonstrates high fidelity, with an RMS of 0.38%. The study outlines potential scenarios for managing PI pool dynamics and emphasizes the inclusion of external variables and feedback loops to enhance model accuracy.

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Simulation Model for First-Time NIH Principal Investigators: Insights and Future Directions

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  1. PI Aging Simulation Model J. Chris White, Twilighttraining.com Walter T. Schaffer, Ph.D. OER, OD, NIH June 4, 2008

  2. RPG PI Stocks

  3. Basic Structure for Age Group New PIs (i.e., first-time) that enter the NIH pool in this age group. Stock - Represents the number of PIs in the total pool that are in this age group. PIs in the system that have “aged” enough to move to the next age group. PIs in the system that have “aged” enough to move into this age group. PIs who experienced a gap in funding now returning into this age group. PIs of this age group that do not get funded the following year.

  4. Connecting Age Groups

  5. SimBLOX Methodology Simulation “agent” model SimBRIX “icon”

  6. SimBLOX Example SimBRIX Drag-and-drop SimBRIX to build larger model Input parameters for selected SimBRIX

  7. Simulation Results – FY1980

  8. Simulation Results – FY2005 RMS = 0.38%

  9. Avg Ages: FY80 = 39.0 FY85 = 41.1 FY90 = 43.7 FY95 = 45.8 FY00 = 47.8 FY05 = 49.5 FY10 = 50.9 FY15 = 52.1

  10. Conclusions and Next Steps • Simulation matches historical data with high fidelity over 25 years: • RMS = 0.38% for worst fit for FY • RMS = 0.011% for total PI’s for simulation • Incorporate external variables as necessary: • Ex: NIH annual budget, success rates • Add “feedback loops” into model structure: • Relationships among key variables and flow rates (e.g., as success rate increases, how do new or funded PI’s respond) • Relationship of influx to efflux in various budget climates • Develop Scenarios for more or fewer New Investigators on total PI Pool • Develop Scenarios to estimate effect of switching to Early Stage Investigators

  11. Add Entrance/Exit Feedback Loop: Balance Influx and Efflux New PIs (i.e., first-time) that enter the NIH pool in this age group. PIs who experienced a gap in funding now returning into this age group. PIs of this age group that do not get funded the following year.

  12. Bachrach, Christine (NIH/NICHD) Barr, Robin (NIH/NIA) Bartrum, John (NIH/OD) Berg, Jeremy (NIH/NIGMS) Boyle, Michael (NIH/OD) Braveman, Norman (NIH/NIDCR) Bronson, Charlette (NIH/NIA) Charles Sherman Clark, Rebecca (NIH/NICHD) DeLeo, James (NIH/CC/DCRI) Dumais, Charles (NIH/CSR) Glanzman, Dennis (NIH/NIMH) Glavin, Sarah (NIH/NIDCR) Khachaturian, Henry (NIH/OD) Lederhendler, Israel (NIH/OD) Lyster, Peter (NIH/NIGMS) McGarvey, Bill (NIH/OD) Moore, Robert F. (NIH/OD) Myers, Louise (NIH/OD) Norvell, John (NIH/NIGMS) O'Connor, Judit (NIH/OD) Onken, James (NIH/NIGMS) Preusch, Peter (NIH/NIGMS) Schaffer, Walter (NIH/OD) Schwartz, Joan (NIH/OD) Sutton, Jennifer (NIH/OD) Suzman, Richard (NIH/NIA) Thakur, Neil (NIH/OD) Members of Workgroup

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