Decision tree models are widely used in health economic evaluation to compare alternative healthcare interventions under uncertainty. These models are particularly useful for performing cost-effectiveness analysis (CEA) and cost-utility analysis (CUA) where outcomes are measured in quality-adjusted life years (QALYs). This tutorial demonstrates how to build a reproducible decision tree model in R using the rdecision package. The example illustrates a simplified pharmacoeconomic comparison between low molecular weight heparin (LMWH) and conventional treatment for patients undergoing hip replacement surgery. The tutorial walks through the process of defining decision nodes, chance nodes, and terminal nodes, assigning probabilities and costs to model pathways, and attaching health utilities to terminal outcomes. The model is evaluated to estimate expected costs and QALYs for each intervention and to calculate the incremental cost-effectiveness ratio (ICER). This example provides a step-by-step introduction to decision tree modelling in R, making it useful for students and researchers interested in Pharmacoeconomics, health technology assessment (HTA), and health data science. Keywords Decision tree modelling, cost-utility analysis, cost-effectiveness analysis, QALY, ICER, pharmacoeconomics, health technology assessment, R programming, health economic modelling, reproducible research.

Decision trees can be created intuitively in R using the `rdecision` package. The creation follows a logic that is easy to follow. The yield is a visual tree output, in addition to the expected costs and benefits for each alternative required for ICER calculation.