General Information

Natural history models project the course of a disease over time and can be used to model the impact of a screening strategy with subsequent treatment on disease progression.1 They are an imperfect yet important tool to help quantify and articulate the value of a healthcare intervention when research is not feasible to produce desired data.1 CISNET models, primarily CRC-SPIN, are used in colorectal cancer (CRC) screening decision-making.2

A model can be defined as a “simplified representation of reality that captures some of that reality’s essential properties and relationships (eg, logical, quantitative, cause/effect).”3

CISNET models, especially CRC-SPIN, are used to inform healthcare policy makers on relative impact of CRC screening tests2


Decision-makers have questions about clinical or economic data that is yet to be generated and that cannot be feasibly answered via research1

  • Statistical and mathematical models are used to:1
    • Estimate clinical outcomes based on data-driven inputs and assumptions
    • Compare results of different strategies with respect to benefits, risks, costs, and/or other parameters
    • Inform research directions, clinical applications, payer/policy decisions, etc.
  • Multiple types of models are used to inform various stakeholders, such as clinicians, policy makers, third-party payers, and hospital administrators1

Models are important tools to help quantify and articulate the value of a healthcare intervention to internal and external stakeholders1

Types of Simulation Models1,3

  • Extrapolation based on current properties
  • Evidence synthesis (e.g. network meta-analysis, individual-patient data meta-analysis)
  • Health risk / outcomes models
  • Survival analysis models
Mathematical Models
  • Extrapolation based on relationships between data
  • Often use statistical models as inputs
  • In Health Economics and Outcomes Research (HEOR), often oriented toward supporting decision-making, and called, “decision-analytic” modeling
  • Usually the basis of budget impact modeling and economic evaluation in HTA
  • Includes many forms including:
    • Simple decision trees
    • Markov, semi-Markov models
    • Microsimulation models and others 
  • Often met with skepticism, as there is little direct evidence to suggest mathematical modeling leads to better decisions about health4-6
  • A key limitation for models is their reliance on historical data to model future outcomes rather than relying on direct real-world evidence3
  • Therefore, the real advantage of modeling is ability to make decisions more transparent and provide an opportunity for legitimacy and accountability among for decision makers7,8


Simulation model infographic
  • MISCAN, SimCRC, and CRC-SPIN are flexible disease models that utilize microsimulation to virtually simulate a large population of patients’ lifetimes2
  • Models inform healthcare policy makers on relative impact of CRC screening tests:9
    • Predict LYG (cost effectiveness)
    • Decreases in CRC incidence
    • CRC related mortality
    • Number of screening tests required
    • Complications arising from screening
  • Models differ slightly in terms of absolute outcomes:9
    • Most effective CRC screening offers the greatest life-years gained compared with no screening
    • Colonoscopy used as an indicator of resource use and risks
    • Total colonoscopies in lifetime
Learn more about the full Indications/Contraindications for the mt-sDNA test.   Please see complete prescribing information for Cologuard in the Cologuard Clinician Brochure

CISNET – Cancer Intervention and Surveillance Modeling Network; CRC-SPIN - Colorectal Cancer Simulated Population model for Incidence and Natural history


MapleHealth workshop on Health Economic and Outcomes Research, November 8th 2019.

CISNET modeling approach. National Cancer Institute website. Accessed April 22, 2023.

Stahl JE. Modelling methods for pharmacoeconomics and health technology assessment: an overview and guide. Pharmacoecon. 2008;26(2):131–148.

Fone D, Hollinghurst S, Temple M, et al. Systematic review of the use and value of computer simulation modelling in population health and health care delivery. J Public Health Med. 2003 Dec;25(4):325–335.

Kuntz KM, Tsevat J, Weinstein MC, et al. Expert panel vs decision-analysis recommendations for post discharge coronary angiography after myocardial infarction. JAMA. 1999;282(23):2246–2251.

Grove WM, Meehl PE. Comparative efficiency of informal (subjective, impressionistic) and formal (mechanical, algorithmic) prediction procedures: the clinical-statistical controversy. Psychology, Public Policy, and Law. 1996;2:293–323.

Dowie J. Evidence based medicine. Needs to be within framework of decision making based on decision analysis. BMJ. 1996;313(7050):170–171.

Dowie J. “Evidence-based”, “cost-effective” and “preference-driven” medicine: decision analysis based medical decision making is the pre-requisite. J Health Serv Res Policy. 1996;1(2):104–113.

Knudsen AB, Zauber AG, Rutter CM, et al. Estimation of benefits, burden, and harms of colorectal cancer screening strategies: Modeling study for the US Preventive Services Task Force. JAMA. 2016;315(23):2595-2609.

Last updated: 04/22/2023