Скачать презентацию PI Aging Simulation Model J Chris White Twilighttraining Скачать презентацию PI Aging Simulation Model J Chris White Twilighttraining

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PI Aging Simulation Model J. Chris White, Twilighttraining. com Walter T. Schaffer, Ph. D. 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 New PIs (i. e. , first-time) that enter the Basic Structure for Age Group New PIs (i. e. , first-time) that enter the NIH pool in this age group. PIs in the system that have “aged” enough to move into 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 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

Sim. BLOX Methodology Simulation “agent” model Sim. BRIX “icon” Sim. BLOX Methodology Simulation “agent” model Sim. BRIX “icon”

Sim. BLOX Example Sim. BRIX Drag-and-drop Sim. BRIX to build larger model Input parameters Sim. BLOX Example Sim. BRIX Drag-and-drop Sim. BRIX to build larger model Input parameters for selected Sim. BRIX

Simulation Results – FY 1980 Simulation Results – FY 1980

Simulation Results – FY 2005 RMS = 0. 38% Simulation Results – FY 2005 RMS = 0. 38%

Avg Ages: FY 80 = 39. 0 FY 85 = 41. 1 FY 90 Avg Ages: FY 80 = 39. 0 FY 85 = 41. 1 FY 90 = 43. 7 FY 95 = 45. 8 FY 00 = 47. 8 FY 05 = 49. 5 FY 10 = 50. 9 FY 15 = 52. 1

Conclusions and Next Steps n Simulation matches historical data with high fidelity over 25 Conclusions and Next Steps n Simulation matches historical data with high fidelity over 25 years: n n n Incorporate external variables as necessary: n n Ex: NIH annual budget, success rates Add “feedback loops” into model structure: n n RMS = 0. 38% for worst fit for FY RMS = 0. 011% for total PI’s for simulation 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 New PIs (i. e. , first-time) 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 n n n n Bachrach, Christine (NIH/NICHD) Barr, Robin (NIH/NIA) Bartrum, Members of Workgroup n n n n 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) De. Leo, James (NIH/CC/DCRI) Dumais, Charles (NIH/CSR) Glanzman, Dennis (NIH/NIMH) Glavin, Sarah (NIH/NIDCR) Khachaturian, Henry (NIH/OD) n n n n Lederhendler, Israel (NIH/OD) Lyster, Peter (NIH/NIGMS) Mc. Garvey, 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)