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The IVI-RA Value Tool is an interactive tool that provides decision-makers with information on the value of different pharmaceutical treatment options for moderate to severe rheumatoid arthritis (RA). The IVI-RA Value Tool is designed to support population-level decisions around how to best allocate limited health care dollars. For examples of different potential applications of the tool, or to learn more about how the model works, see our About page. By proceeding, you acknowledge and accept all terms and conditions .
The IVI-RA Value Tool is driven by the IVI-RA model and is part of the Innovation and Value Initiative’s Open Source Value Project, which seeks to advance consensus-driven approaches to measuring value in the healthcare system. The IVI-RA Value Tool is only one of several ways to interact with the IVI-RA model and participate in the Open Source Value Project. For other ways to access the IVI-RA Model, including running fully customizable analyses with the iviRA R Package and the IVI-RA Model Interface, click here.
The IVI-RA Value Tool simulates the average lifetime value of treatments for a population of patients with moderate to severe RA. The results of the simulation depend on a number of factors including the characteristics of the patient population, the treatments used, and the costs of drugs. Setup the model below.
Are patients using their first bDMARD or have they previously been treated with a bDMARD that failed to work? bDMARDs are biologic disease-modifying anti-rheumatic drugs.
Choose up to three RA treatment sequences below. As a modeled patient’s RA progress over their lifetime, they will move from each treatment to the next, in order, based on effectiveness, side effects, and other factors. Each treatment or treatment combination will occur in sequence, without overlap. Simulated patients will progress through these treatments in order, from left to right and top to bottom in the fields below (same order as reading this page).
To add a treatment to a sequence: Click in a field below to see the drop-down menu. (Note: Be sure to click in the white space; clicking on a treatment in the field will select the treatment.) Select treatments from the drop-down menu to add them to the sequence in the order you want them to occur.
To remove treatments from a sequence: Pressing the delete or backspace key while the dropdown menu is open will delete the last treatment in the sequence. You can also click on a specific treatment in the sequence and press delete to remove it.
The prices below reflect drugs' wholesale acquisition cost (WAC), which is the manufacturer's list price to wholesalers. The actual price of drugs in the marketplace is equal to the WAC minus a discount, however. In the table below, you can directly edit the price (currently based on the WAC), the rebate (as a fraction of the price), and the cost of administration for drugs that must be administered by infusion.
This should take between 10 to 30 seconds.
The model produces results that can be used to examine the value of pharmaceutical treatments for moderate to severe RA:
The IVI-RA model simulates health and economic outcomes averaged across patients in a population. These outcomes are the results generated by the model itself. These are the results you will see next, and are under the Outcomes tab above.
By combining the information produced on patients’ health, costs, and other factors, there are various ways to calculate and compare value. The IVI-RA Value Tool includes two different approaches decision-makers in the health system might use when thinking about how to best spend healthcare dollars: cost-effectiveness and multi-criteria decision analysis. Both approaches are included under the Value tab.
There are a number of ways to calculate value and the Value tab may therefore not incorporate all elements that matter to decision-makers– for example, how different treatments impact the American economy. To examine the impacts of different approaches to calculating value, be sure to check out the Explore page.
The model simulates clinical and economic outcomes over patients' lifetimes. The following results show the lifetime outcomes predicted by the model, based on your selections from the Setup page.
Note: The charts below report the mean results for each treatment sequence. The black bars indicate 95% credible intervals, or, the range of values in which the simulated mean values would lie with 95% probability. For a specific mean value and credible interval, hover your mouse over a bar above
Note: The mean number of serious infections are per 1,000 patients.
Cost-effectiveness analysis (CEA) is a well-established approach for comparing the costs and benefits of alternative treatments. The cost-effectiveness of a treatment is typically assessed relative to a comparator, such as the current standard of care. When a new treatment is more expensive than the comparator but improves health, its value can be assessed using the incremental cost-effectiveness ratio (ICER), which divides the cost increase by the gain in health. The gain in health is frequently measured using quality-adjusted life-years (QALYs), which is a measure that combines life expectancy and quality of life.
To determine whether a treatment sequence is cost-effective, a decision-maker must place a value on a QALY, also know as their willingness to pay. For example, if the willingness to pay for a QALY was $150,000, then a treatment would be deemed cost-effective (relative to the comparator) if the added cost was less than $150,000 per QALY gained. Choose the willingness to pay in the box below.
A criticism of cost-effectiveness analysis (CEA) is that it may not be able to incorporate all of the dimensions of treatment that are relevant to a decision-maker. An alternative approach to value assessment is multiple-criteria decision analysis (MCDA), which lets decision-makers weight different criteria (e.g., health outcomes, treatment attributes, costs) based on their importance to them.
MCDA works by converting performance on each of the included criteria to a single, common scale – in this case, a number between 0 and 100. Each one of the criteria is then given percentage weight that represents its importance relative to the other criteria, and these weights are then applied to arrive at a single weighted average score for each treatment sequence.
To see how this approach works, apply your own preference weights using the sliders for the criteria below.
Use the sliders to select the number of points (0-10) to assign to the criteria. Weights for each criterion (shown in the boxes to the right) are calculated by dividing each criterion's points by the sum of points across all criteria.
Note: The charts above report the mean results for each treatment sequence. The black bars indicate 95% credible intervals, or, the range of values in which the simulated mean values would lie with 95% probability. For a specific mean value and credible interval, hover your mouse over a bar above.
The Outcomes and Value pages provide a starting point for understanding the relative value of RA therapies, but these results may fail to include factors that matter to some decision-makers. For example, insurers may focus on average health outcomes and medical costs across their population, patients may have preferences for treatments with certain attributes, and employers may be interested in effects on worker productivity. The interactive plots below allow you to explore how incorporating these different elements affect decision-making.
Models usually report outcomes averaged across a population, but there are wide ranges in how patients respond to treatments. Select from the options below to see results for different levels of response.
By default, our model provides results for treatment of RA patients over their lifetimes, but decision-makers may be interested in value over a shorter timeframe. To adjust the timeframe for your results, move the slider below.
Medical therapies provide value to healthy people by reducing the risks posed by a future possible diagnosis of illness. In effect, knowing a therapy exists is like having flood insurance for your home – you might never need it, but you benefit from knowing you are protected.
The IVI-RA Value Tool is a web application for assessing the value of disease-modifying anti-rheumatic drugs (DMARDs) for the treatment of moderate to severe rheumatoid arthritis (RA) developed as part of the Open-Source Value Project (OSVP). The OSVP is an open, collaborative, and consensus-driven process for developing open-source models for value assessment of medical interventions. The IVI-RA Value Tool is powered by the IVI-RA model, an open-source simulation model written in R and C++.
OSVP models, such as the IVI-RA model, are intended to serve as “laboratories” for engaging diverse stakeholders in constructive discussion about value assessment and improving the science of modeling and measuring value. There are many ways to provide feedback, make suggestions, and get involved in moving this work forward – see here for more information.
The OSVP is a project of the Innovation and Value Initiative (IVI). IVI is a collaboration among thought leaders in academia, patient advocacy organizations, payers, life sciences companies, providers, delivery systems and other organizations dedicated to preserving innovation, value, and choice in the healthcare system. Our mission is to improve the way value is measured and rewarded in the healthcare system, with the goal of promoting the development and use of high value interventions that advance health.
The IVI-RA modeling platform consists of the following software in addition to the IVI-RA Value Tool:
Source code for the underlying model is available on GitHub.
The IVI-RA model simulates the lifetime costs, health outcomes, and risks associated with sequences of disease-modifying anti-rheumatic drugs (DMARDs) including conventional DMARDs (cDMARDs), biologic DMARDs (bDMARDs), and Janus kinase/STAT pathway inhibitors. The primary measure of disease burden is the Health Assessment Questionnaire (HAQ) disability index, which is a measure of functional status among patients with RA. Patients with worse disability have lower quality-of-life, lower life expectancy, and are more likely to be hospitalized.
Detailed documentation of methods and sources of evidence is available here.
Since the model simulates individual patients within a population, all results are conditional on the characteristics of the population considered. For example, simulated outcomes differ by gender, age, treatment histories, disease activity, and other factors. However, it is important to note that outcomes in the model are not reported for individual patients, but are averages across patients in the specified population.
In the IVI-RA Web Tool you can control age and gender, but other variables are set to their default values. Users who would like to fully customize their analysis should use the iviRA R package or the IVI-RA Model Interface.
There are many ways to build a value assessment model–what experts call structural uncertainty. And, there is always debate over the size and direction of key relationships in it –what experts call parameter uncertainty . Estimates of uncertainty in the IVI-RA Value Tool only account for parameter uncertainty and are based on probabilistic sensitivity analysis (PSA). Users can assess structural uncertainty by exploring any of the 384 model structures available in the iviRA R package and the IVI-RA Model Interface.
Currently, we use the following model structure in the IVI-RA Value Tool: (1) relationship between treatment and HAQ during the initial treatment phase based on Treatment -> ACR -> HAQ, (2) treatment switching during the initial treatment phase based on Treatment -> ACR -> Switch, (3) HAQ progression in the absence of cDMARDs based on the latent class growth model, (4) treatment discontinuation due to all causes using a generalized gamma distribution, and (5) use of the Hernandez-Alava (2013) mixture model.
To ensure that simulated outcomes reflect outcomes in routine practice, baseline events rates (i.e., the rate of disease progression, the mortality rate, the rate at which patients discontinue treatment), patient preferences, and costs are modeled using real-world data. To enhance validity, relative treatment effects (e.g., relative risks, odds ratios, and hazard ratios) are, when possible, based on randomized clinical trials (RCTs).
The IVI-RA Value Tool is developed and maintained by Devin Incerti, Ming Xu, and Jeroen Jansen
One of the goals of the IVI-RA Value Tool (and the IVI-RA model more generally) is to inform decision-making. Health care decision-makers require high-quality evidence on treatment benefits and cost to make coverage decisions, negotiate prices, and make treatment decisions. The current version of the IVI-RA Value Tool is designed to support population-level decisions around how to best allocate limited health care dollars, but the information it provides is valuable to a wide range of stakeholders making many different kinds of decisions. For example:
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