Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Models of Health Promotion and Illness Prevention II01:18

Models of Health Promotion and Illness Prevention II

The person's health status fluctuates continually, varying from being in good health to becoming ill and returning to being healthy. To understand the concept of illness prevention, there are two models. First, the health-illness continuum model is a graphic representation of an individual's wellness. It states that a person is considered healthy in the absence of physical disease and the presence of good emotional health.
The agent-host-environment model states that disease results from...
Pharmacodynamic Models: Overview01:27

Pharmacodynamic Models: Overview

Pharmacodynamic (PD) responses describe the interaction between a drug and its biological target, culminating in a physiological effect. These responses can be classified into different types: continuous variables, such as blood glucose levels; categorical outcomes, like survival rates; and time-to-event metrics, such as disease progression. Understanding and modeling PD responses are critical for optimizing drug efficacy and safety.PD models describe the relationship between drug concentration...
Models of Health Promotion and Illness Prevention I01:25

Models of Health Promotion and Illness Prevention I

A model is a theoretical way to understand a concept or an idea. Models can overcome barriers to health regardless of diverse economic and cultural backgrounds. In addition, models make the task easier by providing different ways to approach complex issues. There are two major health promotion models: the health belief model and the health promotion model.
The health belief model (HBM) attempts to predict health-related behavior in specific belief patterns. According to the HBM, a person's...
Impact of Pharmacokinetic–Pharmacodynamic Models: Regulatory Decisions01:15

Impact of Pharmacokinetic–Pharmacodynamic Models: Regulatory Decisions

PK–PD modeling has significantly influenced FDA regulatory decisions, particularly drug approval, dosage optimization, and labeling. These models integrate pharmacokinetics (PK) and pharmacodynamics (PD) to predict drug behavior and effects, aiding in optimizing dosing regimens and enhancing the probability of clinical trial success.One notable example is Nesiritide (Natrecor®), a recombinant human brain natriuretic peptide for treating acute decompensated congestive heart failure (CHF).
Pharmacodynamic Models: Direct Effect Model and Indirect Response Model01:29

Pharmacodynamic Models: Direct Effect Model and Indirect Response Model

Pharmacodynamic models are essential tools in understanding the relationship between drug concentrations and their effects on biological systems. By characterizing the dynamics of drug action, these models guide dose selection, optimize therapeutic efficacy, and inform the development of new drugs. Two major classes of pharmacodynamic models include direct effect and indirect response models.Direct Effect ModelsDirect effect models describe the immediate relationship between drug concentration...
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least squares (OLS)...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Temporal and geographic analyses of colorectal cancer screening during and after the COVID-19 pandemic in a federally qualified health center.

PloS one·2026
Same author

Projected Demographic Trends in the Likelihood of Having or Becoming a Dementia Family Caregiver in the U.S. Through 2060.

Populations·2026
Same author

CRC-SPIN version 3.0: an updated policy model for colorectal cancer screening that includes the serrated pathway.

Journal of the National Cancer Institute·2026
Same author

Discrete-Event Simulation Modeling Framework for Cancer Interventions and Population Health in R (DESCIPHR): An Open-Source Pipeline.

PharmacoEconomics·2026
Same author

The Centers for Medicare and Medicaid Services and others misunderstand stool testing for colorectal cancer.

Journal of the National Cancer Institute·2025
Same author

Modeled Cost-Effectiveness of a Rideshare Program to Facilitate Colonoscopy Completion.

JAMA network open·2025

Related Experiment Video

Updated: Jun 12, 2026

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

Dynamic microsimulation models for health outcomes: a review.

Carolyn M Rutter1, Alan M Zaslavsky2, Eric J Feuer3

  • 1Biostatistics Unit, Group Health Research Institute, Seattle, WA USA, and Department of Biostatistics, University of Washington School of Public Health and Community Medicine, Seattle, WA USA (CMR)

Medical Decision Making : an International Journal of the Society for Medical Decision Making
|May 21, 2010
PubMed
Summary
This summary is machine-generated.

Microsimulation models (MSMs) simulate individual health events to assess population-level treatment effects. Improved communication and resources are needed to advance MSM methods and their application in health policy decisions.

Related Experiment Videos

Last Updated: Jun 12, 2026

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

Area of Science:

  • Health economics
  • Epidemiology
  • Health policy analysis

Background:

  • Microsimulation models (MSMs) simulate individual health trajectories for disease processes.
  • MSMs aggregate individual data to estimate population-level treatment impacts and comparative effectiveness.
  • Limited communication among modelers and resources hinder methodological advancements in MSMs.

Purpose of the Study:

  • To provide an overview of microsimulation modeling for health policy.
  • To guide the development and application of MSMs in health research.
  • To address the need for better understanding and improved methods in MSM utilization.

Main Methods:

  • Overview of microsimulation modeling principles and components.
  • Discussion of parameter selection methods, including calibration.
  • Exploration of validation techniques and reporting standards (sensitivity analyses, variability, transparency).

Main Results:

  • MSMs are valuable tools for simulating health outcomes and treatment effects.
  • Calibration and validation are crucial for ensuring MSM reliability.
  • Model transparency and reporting of variability enhance the interpretability of MSM findings.

Conclusions:

  • Microsimulation models are increasingly vital for informing health policy decisions.
  • Enhanced understanding and improved methodologies are essential for effective MSM application.
  • Continued development in MSM approaches will strengthen their role in evidence-based policy.