# A Simulation Model-based Framework to Support Oncology Guidelines and Practice

> **NIH NIH K99** · GEORGETOWN UNIVERSITY · 2021 · $175,392

## Abstract

Abstract
Personalized cancer care is complex in this unprecedented era of discovery and big data. The large and evolving
knowledge base requires clinicians and policymakers to synthesize diverse data to design new trials, inform
clinical guidelines, practice, and policy. The Institute of Medicine and others have recommended the use of
simulation modeling in these situations to synthesize evidence and support clinical care. Simulation modeling
involves the use of mathematical models to combine various sources of evidence to assess how interventions
could alter the progression of a disease and affect outcomes. My overarching goal is to use this K99/R00 award
to gain the necessary skills and experience to become an independent researcher and leader in the use of
simulation modeling in oncology.
This revised application includes training in modeling methods needed to fill gaps in my knowledge and use
that training to build a new simulation model to address gene expression profile (GEP)-guided care for the
nearly 180,000 US women annually diagnosed with early-stage breast cancer. The training aims are: Aim K1.
Apply training in data discovery and synthesis to develop input parameters to model the effects of chemo-
endocrine (vs. endocrine) therapy on cancer outcomes such as recurrence, mortality, and chemotherapy-
related toxicity based on patient (e.g., age, race, comorbidity) and tumor (e.g., tumor size, grade)
characteristics, and GEP test results; Aim K2. Apply training in simulation modeling to build a model
combining input parameters from aim K1 to project cancer outcomes; and Aim K3. Apply training in
uncertainty quantification to estimate the variability associated with modeled outcomes to place results in
context for clinicians and guideline developers. The research aims are: Aim R1. Perform model validation to
demonstrate the model's ability to reproduce predictions using an independent data source that was not used
in aim K1; make necessary revisions to the model; Aim R2. Use the validated model to provide a summary of
the balance of benefits and harms of chemotherapy in exemplar groups of women to support the development
of clinical guidelines; and Aim R3. Create an interactive web-interface to provide model results on the effects of
chemo-endocrine (vs. endocrine) therapy on cancer outcomes based on individual characteristics.
I am uniquely qualified for this award given my strong track record of pilot funding, 21 high impact
publications, a solid quantitative research foundation, commitment to a research career, and preliminary
modeling research with my multidisciplinary mentoring team. The exceptional institutional resources coupled
with the Cancer Intervention and Surveillance Modeling Network (CISNET) provide the ideal setting for this
application. The integrated research and training will leave me poised to become an independent researcher
using simulation modeling to assist in the translation of rapidly evolving knowledge into oncol...

## Key facts

- **NIH application ID:** 10129921
- **Project number:** 5K99CA241397-02
- **Recipient organization:** GEORGETOWN UNIVERSITY
- **Principal Investigator:** Jinani Jayasekera
- **Activity code:** K99 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $175,392
- **Award type:** 5
- **Project period:** 2020-04-01 → 2022-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10129921

## Citation

> US National Institutes of Health, RePORTER application 10129921, A Simulation Model-based Framework to Support Oncology Guidelines and Practice (5K99CA241397-02). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/10129921. Licensed CC0.

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