For example ‘ read.csv("life-table.csv", header=TRUE)’ reads in the life table csv data in the working directory, specifying that the first row of the csv file is the header row. R “as is”) allows you to read in data using the ‘read.csv()’ function, where the file path relative to the working directory can be specified within the brackets. In this tutorial, we read in data files that contain life table data and outputs of survival analyses. The folder containing the RProject files acts as the “MS Excel file”, where the separate csv files and R scripts within that folder are similar to having different MS Excel sheets within the file, housing different data tables or analysis functions for the model. The RProject is the health economic evaluation model. There are many blogs and guides on how to link Git and RStudio using R projects. Clicking on the Git “R Project” item automatically sets your working directory to be the equivalent to where the project is based, allowing you to read in data also within that folder. Once the folder has been downloaded (which can be done through the “code” button) or linked via another interface, such as RStudio, you can run the R project. Throughout this tutorial code, data available from the GitHub repository are cited. By focusing on using basic R functions, rather than specific health economics R packages, it reduces reliance on “black boxes” and increases the potential for adaptability to suit need. We outline how to conduct these analyses using mainly base R functions. This is then followed by instructions on how to conduct analyses for the expected value of perfect information (EVPI) and the expected value of partially perfect information (EVPPI), also known as the expected value of perfect parameter information, within R. This case study is then used to demonstrate how to integrate survival analyses within sensitivity analyses using R instead of MS Excel. This tutorial first introduces a case study of hip replacement surgery, for which an MS Excel model has been published. However, a comparison of more advanced modelling techniques, such as modelling heterogeneity through the inclusion of survival analysis results whilst conducting value of information (VOI) analyses in R compared to MS Excel, has yet to be done. Previous health economic evaluation tutorials for R generally run through how to create deterministic and probabilistic Markov models in R. Additionally, decomposition techniques can be utilised to allow for covariance to be maintained during probabilistic sensitivity analyses. Subsequently, these impacts can be fed through Markov models to appropriately account for heterogeneity across subpopulations of interest. Intervention impacts on health outcomes, conditional on patient characteristics, can be quantified through standard survival analysis techniques. Markov models can quantify the impact of interventions on transitions between health states, as well as the costs and outcomes associated with the differing course of actions. The foundation of many such health economic evaluations is often the Markov model. Whilst Microsoft (MS) Excel and TreeAge are visual graphical user interfaces and therefore useful software for learning purposes, R (alongside other programming language-based software such as MATLAB) has higher efficiency, transparency and adaptability in comparison. The benefits of utilising R for health economic evaluations are becoming well documented.
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