This tutorial describes how to run the IVI-RA model. The model is a discrete-time individual patient simulation (IPS) with 6-month model cycles that simulates patient outcomes over a lifetime or a specified time period. The model simulates the costs, health outcomes, and risks associated with disease-modifying anti-rheumatic drugs (DMARDs) including conventional DMARDs (cDMARDs), biologic DMARDs (bDMARDs), and Janus kinase/signal transducers and activators of transcription (JAK/STAT) inhibitors. The primary measure of disease burden is the health assessment questionnaire (HAQ) score, which is a measure of function among patients with rheumatoid arthritis.
The model accounts for both parameter and structural uncertainty. Parameter uncertainty is quantified using probabilistic sensitivity analysis (PSA), which propagates uncertainty in input parameters throughout the model by randomly sampling the parameters from their (joint) probability distribution and running the model for each sampled parameter set. Unlike most models, the IVI-RA model does not commit to a single model structure, but instead allows for a number of scientifically defensible approaches. Since the model can be run for any of the model structures, structural uncertainty can be evaluated by comparing estimates of value across the different structures. Because the model is an IPS that allows for both PSA and structural uncertainty analysis, the simulation is mostly written in C++ so that it can be run in a reasonable amount of time.
Users wishing to learn more about the model should consult the model description.
iviRA package can be installed from GitHub using the
To use the model, load the installed package into R.
The model can be simulated within the
iviRA package using the
sim_iviRA function. The simulation depends on three primary factors: (1) the population considered, (2) the selected model structures, and (3) the values of the parameters. The steps needed to run the model and perform a value assessment can consequently be summarized as follows:
Click next to continue the tutorial and learn about step 1.