Health Economic Decision Tree Models of Diagnostics for Dummies: A Pictorial Primer

Abstract

Health economics is a discipline of economics applied to health care. One method used in health economics is decision tree modelling, which extrapolates the cost and effectiveness of competing interventions over time. Such decision tree models are the basis of reimbursement decisions in countries using health technology assessment for decision making. In many instances, these competing interventions are diagnostic technologies. Despite a wealth of excellent resources describing the decision analysis of diagnostics, two critical errors persist: not including diagnostic test accuracy in the structure of decision trees and treating sequential diagnostics as independent. These errors have consequences for the accuracy of model results, and thereby impact on decision making. This paper sets out to overcome these errors using color to link fundamental epidemiological calculations to decision tree models in a visually and intuitively appealing pictorial format. The paper is a must-read for modelers developing decision trees in the area of diagnostics for the first time and decision makers reviewing diagnostic reimbursement models.

Reflex ROS1 IHC Screening with FISH Confirmation for Advanced Non-Small Cell Lung Cancer—A Cost-Efficient Strategy in a Public Healthcare System

“…A test-based decision modeling approach was developed using ROS1 IHC, FISH, and NGS sensitivity and specificity in addition to the prevalence of ROS1 fusions. For IHC screening, positive predictive values (PPV) and negative predictive values (NPV) were calculated for each arm of the model to determine the proportion of patients who would first test positive after IHC screening and the subsequent proportion of patients who would undergo FISH confirmation (Figure 1, Tables S1-S9) [15]. Two models were developed with the calculated parameters to compare cost to true positive rates and cost to turnaround time of eight diagnostic testing strategies in patients with advanced nonsquamous NSCLC (Figure S1).…”

Mathematical modelling to estimate the impact of maternal and perinatal healthcare services and interventions on health in sub-Saharan Africa: A scoping review

“…Of the remaining studies in the sample, most utilised a decision tree/analytic model (n=7). These models, commonly used in health economic analysis, represent potential treatment pathways as a series of ‘branches’ demarcated by decision nodes, representing some decision of interest (e.g., the decision to deliver a screening intervention) and chance nodes from which mutually exclusive probabilities of a set of outcomes following a decision are represented (87,88). Other types of models were utilised much less frequently with only 5 (8%) studies employing an individual-based framework (27,32,34,68,86).…”

Economic Analyses of Respiratory Tract Infection Diagnostics: A Systematic Review

“…Decision tree models are used to calculate the costs and effectiveness outcomes of different clinical interventions, usually over a limited time period as time cannot be modelled explicitly [ 37 ]. A combination of decisions and probability rates of occurrence are used to calculate the outcomes for various cohorts in the model.…”