In MS Excel, these are simply added/viewed as worksheets. External data in the form of life tables (“life-table.csv”) and a parametric Weibull regression analysis (“hazardfunction.csv” and “cov55.csv” containing coefficient and covariance data, respectively) need to be fed into the model. The first is through background mortality and the second through the impact on the risk of failure of primary THR. There are two ways in which age and sex influence transitions within this case study, with mean ages of 40, 60 and 80 years for male and female individuals of interest. We load reshape2 and ggplot2, which are needed to plot the outputs. However, it is good practice to group and load all necessary packages at the top of the script, avoiding potential issues when running parts of your code. To set up the model in R, we first need to load packages, which will be used later for plotting data. Numbered sections refer to subheadings within the relevant R script. Fig.2 2 (see ‘THR_Model.R’ ) to define inputs and produce outputs. In R, we focus on one script that follows the sections described in Fig. Information from different sheets is then combined to produce outputs presented on the analysis sheet. In the MS Excel model, there are different worksheets that house inputs, intermediate values and outputs, including “Parameters” listing the main parameters of the model and “Standard” listing the health states and tracing the cohort across these health states over time for a standard prosthesis (see in Fig. This structure can be utilised in other models outside of the case study. Fig.2, 2, referring to the stated subsection headings and relating these to equivalent MS Excel processes. In this tutorial, we follow the structure outlined in Fig. The Markov model process or probabilistic analysis will not be described in detail here, as these are covered in detail elsewhere. Specifically, “THR_Model.R” and “THR_Model_VOI.R” scripts are equivalent to “Ex57sol.xls” and “E圆6bsol.xls”, respectively. The corresponding MS Excel files are available for download within this repository or originally from the Briggs et al. *Procedures are represented by rectangles whereby primary hip replacement is either the new or standard procedure depending on the respective branch on the decision treeĪll of the R relevant materials used in this tutorial can be found within a corresponding GitHub repository. represents a collapsed node of the decision tree in which the Markov model is repeated. Health states of the model are represented by ovals, transitions between health states are represented by arrows. Total hip replacement (THR) decision model schematic. 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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