Month: December 2018

Updated estimates of cost-effectiveness for plaque psoriasis treatments

Along with co-authors from ICER and The CHOICE Institute, I recently published a paper in JMCP titled, “Cost-effectiveness of targeted pharmacotherapy for moderate-to-severe plaque psoriasis.” In this publication, we sought to update estimates of cost-effectiveness for systemic therapies useful in the population of patients with psoriasis for whom methotrexate and phototherapy are not enough.

Starting in 1998, a class of drugs acting on Tumor Necrosis Factor alpha (TNFɑ) has been the mainstay of psoriasis treatment in this population. The drugs in this class, including adalimumab, etanercept, and infliximab, are still widely used due to their long history of safety and lower cost than some competitors. They are less effective than many new treatments, however, particularly drugs inhibiting interleukin-17 such as brodalumab, ixekizumab, and secukinumab.

This presents a significant challenge to decision-makers: is it better to initiate targeted treatment with a less effective, less costly option, or a more effective, costlier one? We found that the answer to this question is complicated by several current gaps in knowledge. First, there is some evidence that prior exposure to biologic drugs is associated with lower effectiveness in subsequent biologics. This means that the selection of a first targeted treatment must balance cost considerations with the possibility of losing effectiveness in subsequent targeted treatments if the first is not effective.

A related issue is that the duration of effectiveness (or “drug survival”) for each of these drugs is currently poorly characterized in the US context. Drug discontinuation and switching is significantly impacted by policy considerations such as requirements for step therapy and restrictions on dose escalation. Therefore, while there is a reasonable amount of research about drug survival in Europe, it is not clear how well this information translates to the US.

Another difficulty of performing cost-effectiveness research in this disease area is the difficulty of mapping utility weights onto trial outcomes. Every drug considered in our analysis used percentage change in the Psoriasis Area Severity Index (PASI) over baseline. Because this is not an absolute measure, it required that we assume that patients have comparable baseline PASI scores between studies. In other words, we had to assume that a given percent improvement in PASI was equivalent to a given increase in health-related quality of life. This means that if one study’s population had less severe psoriasis at baseline, we probably overstated the utility benefit of that drug.

In light of these gaps in knowledge, our analytic strategy was to model a simulated cohort of patients with incident use of targeted drugs. After taking a first targeted drug, they could be switched to a second targeted drug or cease targeted therapy. We made the decision to limit patients to two lines of targeted treatment in order to keep the paper focused on the issue of initial treatment.

pso cost effectiveness frontier

What we found is a nuanced picture of cost-effectiveness in this disease area. In agreement with older cost-effectiveness studies, we found that infliximab is the most cost-effective TNFɑ drug and, along with the PDE-4 inhibitor apremilast, is likely to be the most cost-effective treatment at lower willingness-to-pay (WTP) thresholds. However, at higher WTP thresholds of $150,000 per quality-adjusted life year and above, we found that the IL-17 inhibitors brodalumab and secukinumab become more likely to be the most cost-effective.

The ambiguity of these results suggests both the importance of closing the gaps in knowledge mentioned above and of considering factors beyond cost-effectiveness in coverage decisions. For example, apremilast is the only oral drug we considered and patients may be willing to trade lower effectiveness to avoid injections. Another consideration is that IL-17 inhibitors are contraindicated for patients with inflammatory bowel disease, suggesting that payers should make a variety of drug classes accessible in order to provide for all patients.

In summary, these results should be seen as provisional, not only because many important parameters are still uncertain, but also because several new drugs and biosimilars for plaque psoriasis are nearing release. Decision-makers will need to keep an eye on emerging evidence in order to make rational decisions about this costly and impactful class of drugs.

Economic Evaluation Methods Part I: Interpreting Cost-Effectiveness Acceptability Curves and Estimating Costs

By Erik Landaas, Elizabeth Brouwer, and Lotte Steuten

One of the main training activities at the CHOICE Institute at the University of Washington is to instruct graduate students how to perform economic evaluations of medical technologies. In this blog post series, we give a brief overview of two important economic evaluation concepts. Each one of the concepts are mutually exclusive and are meant to stand alone. The first of this two-part series describes how to interpret a cost-effectiveness acceptability curve (CEAC) and then delves into ways of costing a health intervention. The second part of the series will describe two additional concepts: how to develop and interpret cost-effectiveness frontiers and how multi-criteria decision analysis (MCDA) can be used in Health Technology Assessment (HTA).

 

Cost-Effectiveness Acceptability Curve (CEAC)

The CEAC is a way to graphically present decision uncertainty around the expected incremental cost-effectiveness of healthcare technologies. A CEAC is created using the results of a probabilistic analysis(PA).[1] PA involves simultaneously drawing a set of input parameter values by randomly sampling from each parameter distribution, and then storing the model results.  This is repeated many times (typically 1,000 to 10,000), resulting in a distribution of outputs that can be graphed on the cost-effectiveness plane. The CEAC reflects the proportion of results that are considered ‘favorable’ (i.e. cost effective) in relation to a given cost-effectiveness threshold.

The primary goal of a CEAC graph is to inform coverage decisions among payers that are considering a new technology, comparing one or more established technologies that may include the standard of care. The CEAC enables a payer to determine, over a range of willingness to pay (WTP) thresholds, the probability that a medical technology is considered cost-effective in comparison to its appropriate comparator (e.g. usual care), given the information available at the time of the analysis. A WTP threshold is generally expressed in terms of societal willingness to pay for an additional life year or quality-adjusted life year (QALY) gained. In the US, WTP thresholds typically range between $50,000 – $150,000 per QALY.

The X-axis of a CEAC represents the range of WTP thresholds. The Y-axis represents the probability of each comparator being cost-effective at a given WTP threshold, and ranges between 0% and 100%. Thus, it simply reflects the proportion of simulated ICERs from the PA that fall below the corresponding thresholds on the X-axis.

Figure 1. The Cost-Effectiveness Acceptability Curve

CEAC

Coyle, Doug, et al. “Cost-effectiveness of new oral anticoagulants compared with warfarin in preventing stroke and other cardiovascular events in patients with atrial fibrillation.” Value in health 16.4 (2013): 498-506.

Figure 1 shows CEACs for five different drugs, making it easy for the reader to see that at the lower end of the WTP threshold range (i.e. $0 – $20,000 per QALY), warfarin has the highest probability to be cost-effective (or in this case “optimal”). At WTP values >$20,000 per QALY, dabigatran has the highest probability to be cost-effective. All the other drugs have a lower probability of being cost-effective compared to warfarin and dabigatran at every WTP threshold. The cost-effectiveness acceptability frontier in Figure 1 follows along the top of all the curves and shows directly which of the five technologies has the highest probability of being cost-effective at various levels of the WTP thresholds.

To the extent that the unit price of the technology influences the decision uncertainty, a CEAC can offer insights to payers as well as manufacturers as they consider a value-based price. For example, a lower unit price for the drug may lower the ICER and, all else equal, this increases the probability that the new technology is considered cost-effective at a given WTP threshold. Note, that when new technologies are priced such that the ICER falls just below the WTP for a QALY, (e.g. an ICER of $99,999 when the WTP is $100,000) the decision uncertainty tends to be substantial, often around 50%. If decision uncertainty is perceived to be ‘unacceptably high’, it can be recommended to collect further information to reduce decision uncertainty. Depending on the drivers of decision uncertainty, for example in case of stochastic uncertainty in the efficacy parameters, performance-based risk agreements (PBRAs) or managed entry schemes may be appropriate tools to manage the risk.

Cost estimates

The numerator of most economic evaluations for health is the cost of a technology or intervention. There are several ways to arrive at that cost, and choice of method depends on the context of the intervention and the available data.

Two broadly categorized methods for costing are the bottom-up methodand the top-down method. These methods, described below, are not mutually exclusive and may complement each other, although they often do not produce the same results.

costs

Source of Table: Mogyorosy Z, Smith P. The main methodological issues in costing health care services: a literature review. 2005.

The bottom-up method is also known as the ingredients approach or micro-costing. In this method, the analyst identifies all the items necessary to complete an intervention, such as medical supplies and clinician time, and adds them up to estimate the total cost. The main categories to consider when calculating costs via the bottom-up method are medical costs and non-medical costs. Medical costs can be direct, such as the supplies used to perform a surgery, or indirect, such as the food and bed used for inpatient care. Non-medical costs often include costs to the patient, such as transportation to the clinic or caregiver costs. The categories used when estimating the total cost of an intervention will depend on the perspective the analyst takes (perspectives include patient, health system, or societal).

The bottom-up approach can be completed prospectively or retrospectively, and can be helpful for planning and budgeting. Because the method identifies and values each input, it allows for a clear breakdown as to where dollars are being spent. To be accurate, however, one must be able to identify all the necessary inputs for an intervention and know how to value capital inputs like MRI machines or hospital buildings. The calculations may also become unwieldy on a very large scale. The bottom-up approach is often used in global health research, where medical programs or governmental agencies supply specific items to implement an intervention, or in simple interventions where there are only a few necessary ingredients.

The top-down estimation approach takes the total cost of a project and divides it by the number of service units generated. In some cases, this is completed simply looking at the budget for a program or an intervention and then dividing that total by the number of patients. The top-down approach is useful because it is a simple, intuitive measurement that captures the actual amount of money spent on a project and the number of units produced, particularly for large projects or organizations. Compared to the bottom-up approach, the top-down approach can be much faster and cheaper. The top-down approach can only be used retrospectively, however, and may not allow for the breakdown of how the money was spent or be able to identify variations between patients.

While the final choice will depend on several factors, it makes sense to try and think through (or model) which of the cost inputs are likely to be most impactful on the model results. For example, the costs of lab tests may most accurately be estimated by a bottom-up costing approach. However, if these lab costs are likely to be a fraction of the cost of treatment, say a million dollar cure for cancer, then going through the motions of a bottom-up approach may not be the most efficient way to get your PhD-project done in time. In other cases, however, a bottom-up approach may provide crucial insights that move the needle on the estimated cost-effectiveness of medical technologies, particularly in settings where a lack of existing datasets is limiting the potential of cost-effectiveness studies to inform decisions on the allocation of scarce healthcare resources.

[1]Fenwick, Elisabeth, Bernie J. O’Brien, and Andrew Briggs. “Cost‐effectiveness acceptability curves–facts, fallacies and frequently asked questions.” Health economics 13.5 (2004): 405-415.