Health-Related Quality of Life in Adolescent & Young Adult Cancer
A Hybrid System Dynamics, Agent-Based, and Network Modeling Approach
Research Overview
The Problem
AYA cancer patients (ages 15–39) report health-related quality of life below general-population norms for years after treatment, with the largest and longest-lasting shortfalls in physical and emotional functioning (Smith 2013, AYA HOPE; Husson 2017). Observational studies sample only a few time points and cannot represent how heterogeneity, support, and services evolve together.
The Gap
A structured triage of 944 records (237 relevant) confirms established use of system-dynamics, agent-based, network, and complex-systems methods in oncology (for example, Kenzie 2023; Stephan 2024). Yet only 9 works show bidirectional cross-method coupling, and none combine an agent-based patient model, a social-support network, and system dynamics for AYA HRQoL.
The Solution
A tri-layer hybrid architecture: System Dynamics (population and services), Agent-Based Modeling (individual heterogeneity), and an evolving social Network (peer support), coupled through six formally specified, independently switchable interfaces.
Tri-Layer Hybrid Architecture
MACRO: System Dynamics
Population feedback loops, service queue, resource constraints
dX/dt = f(X, αmacro)
MESO: Agent-Based Model
Individual patients, heterogeneous attributes, behavioral rules
Agenti = {age, dx, HRQoLi(t)}
MICRO: Network Layer
Evolving peer-support ties; per-run network size drawn from AYA evidence (Cloyes 2022)
G = (V, E, wij)
The methodological contribution is the formally specified, verified, bidirectionally coupled architecture of six switchable interfaces (SW_NA, SW_SA, SW_AS, SW_NS, SW_SN, SW_AN), plus an ablation protocol showing each layer earns inclusion. A "no-leak" verification proves each off switch removes exactly one effect.
Key Results
Note: Verification is complete. Behavioral validity is pattern-oriented. Predictive validity against an external cohort remains open under a pre-registered EORTC data request (see Publications). The model is a theory and decision-support scaffold rather than a tool for individual prognosis.
Literature Corpus
The gap claim rests on a structured, SHA-256–verified triage of the working corpus: every count below is drawn from the triage audit, and every audited source path matches the hash frozen in the corpus record.
Nearest prior art (cited)
The closest partial precedents from the triage, each using one or more of the same methods yet missing the AYA-HRQoL domain or confirmed bidirectional coupling.
System dynamics modeling for cancer prevention and control: A systematic review
PLoS ONE, 2023
Agent-based approaches for biological modeling in oncology: A literature review
Artificial Intelligence in Medicine, 2024
An Introduction to Complex Systems Science and Its Applications
Complexity, 2020
Formalizing the role of agent-based modeling in causal inference and epidemiology
American Journal of Epidemiology, 2014
Systems science and systems thinking for public health
BMJ Open, 2015
Scope note: Counts reflect the working corpus triage as of July 2026 and describe the current state of the literature. The full corpus and the triage records live in the literature-review folder of the project.