Pi aging simulation model
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PI Aging Simulation Model. J. Chris White, Twilighttraining.com Walter T. Schaffer, Ph.D. OER, OD, NIH June 4, 2008. RPG PI Stocks. Basic Structure for Age Group. New PIs (i.e., first-time) that enter the NIH pool in this age group.

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PI Aging Simulation Model

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Pi aging simulation model

PI Aging Simulation Model

J. Chris White, Twilighttraining.com

Walter T. Schaffer, Ph.D. OER, OD, NIH

June 4, 2008


Rpg pi stocks

RPG PI Stocks


Basic structure for age group

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.


Connecting age groups

Connecting Age Groups


Simblox methodology

SimBLOX Methodology

Simulation “agent” model

SimBRIX “icon”


Simblox example

SimBLOX Example

SimBRIX

Drag-and-drop SimBRIX to build larger model

Input parameters for selected SimBRIX


Simulation results fy1980

Simulation Results – FY1980


Simulation results fy2005

Simulation Results – FY2005

RMS = 0.38%


Pi aging simulation model

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


Conclusions and next steps

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


Add entrance exit feedback loop balance influx and efflux

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.


Members of workgroup

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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