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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.

This file contain 3 keys to make conclusions about population only using sample which are, central limit theorem, unbiased predictor parameter, and confidence interval. 1. Central Limit Theorem The Central Limit Theorem states that as the sample size increases, the distribution of the sample mean will tend to follow a normal distribution, regardless of the shape of the original population distribution. 2. Unbiased Parameter Predictor An unbiased estimator is a statistic calculated from sample data that, on average, equals the true value of the population parameter. For example, the sample mean is an unbiased estimator of the population mean, and the sample variance with denominator n−1 is an unbiased estimator of the population variance. 3. Confidence Interval Confidence interval is where we trust how much the interval contain the actual parameter value. For example if we do 95% confidence interval, then we trust that 95% of the interval contain the actual value of parameter, if we take 100 confidence interval then it would likely 95 of them contain parameter and 5 of them arent. The bigger the percentage, itll make the interval wider. Cause it will try to contain more of the parameter. While lower percentage may make the interval narrower cause itll only take a part of the parameter.