Related Experiment Video
Updated: Jul 5, 2026
07:47
Measuring Delay Discounting in Humans Using an Adjusting Amount Task
Published on: January 9, 2016
Handling uncertainty in cost-effectiveness models
1Health Economics Research Centre, University of Oxford, England. andrew.briggs@ihs.ox.ac.uk
Pharmacoeconomics
|September 8, 2000
Summary
This review explores managing uncertainty in economic modeling for cost-effectiveness analysis. It details methods like Bayesian statistics and Monte Carlo simulation to improve result reliability.
Area of Science:
- Health Economics
- Decision Analysis
- Statistical Modeling
Background:
- Economic evaluations frequently utilize modeling to synthesize data from diverse sources.
- Modeling is crucial even when economic evaluations accompany clinical trials.
Purpose of the Study:
- To review the methodologies for handling uncertainty in cost-effectiveness results derived from decision-analytic modeling.
- To enhance the comparability and reliability of economic evaluation outcomes.
Main Methods:
- Defining a 'reference case' with agreed-upon methods for comparability.
- Specifying patient characteristics with experimental study rigor.
- Estimating data requirements using Bayesian statistics and prior distributions for model parameters.
- Employing Monte Carlo simulation for probabilistic analyses of cost-effectiveness.
Main Results:
- Probabilistic analyses, utilizing Monte Carlo simulation, generate distributions of cost-effectiveness.
- Parameter uncertainty is addressed through sampling from prior distributions.
- Modelling uncertainty introduces an additional layer of uncertainty to analysis results.
Conclusions:
- Standardized methods and Bayesian approaches are key to managing uncertainty in economic modeling.
- Accurate specification of patient characteristics and parameter distributions improves model validity.
- Acknowledging both parameter and modeling uncertainty is essential for robust cost-effectiveness conclusions.
Related Concept Videos
03:37Uncertainty in Measurement: Accuracy and Precision
Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value.
00:59Uncertainty: Overview
In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.
01:17Clearance Models: Noncompartmental Models
Clearance is a pharmacokinetic parameter traditionally defined by compartment models, signifying the rate at which a drug is expelled from the body. However, a noncompartmental model offers an alternative method for assessing clearance, primarily employing empirical data obtained after administering a single drug dose.
The noncompartmental approach capitalizes on extensive sampling data, correlating the volume of distribution to systemic exposure and the administered dosage. This method enables...
The noncompartmental approach capitalizes on extensive sampling data, correlating the volume of distribution to systemic exposure and the administered dosage. This method enables...
01:23Mechanistic 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)...
01:26Pharmacokinetic Models: Comparison and Selection Criterion
Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.
01:13Parameters Affecting Nonlinear Elimination: Zero-Order Input, First-Order Absorption and Two-Compartment Model
Drugs administered through various routes can lead to nonlinear elimination, resulting in complex pharmacokinetic behaviors crucial to understanding efficacious drug dosing.
When a drug is administered through a constant intravenous infusion and eliminated via nonlinear pharmacokinetics, it follows zero-order input. For example, oral drugs undergo first-order absorption upon administration and are eliminated through nonlinear pharmacokinetics.
In the case of subcutaneously administered drugs,...
When a drug is administered through a constant intravenous infusion and eliminated via nonlinear pharmacokinetics, it follows zero-order input. For example, oral drugs undergo first-order absorption upon administration and are eliminated through nonlinear pharmacokinetics.
In the case of subcutaneously administered drugs,...