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Dynamic Cost-Effectiveness of Oncology Drugs
Yang Lu, PhD; John R. Penrod, PhD; Neeraj Sood, PhD; Saarah Woodby, BA; and Tomas Philipson, PhD
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Yuri Sanchez, PhD; John R. Penrod, PhD; Xiaoli Lily Qiu, PhD; John Romley, PhD; Julia Thornton Snider, PhD; and Tomas Philipson, PhD
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Dynamic Cost-Effectiveness of Oncology Drugs

Yang Lu, PhD; John R. Penrod, PhD; Neeraj Sood, PhD; Saarah Woodby, BA; and Tomas Philipson, PhD
Objective: To develop a methodology for computing cost-effectiveness measures of a drug throughout its life cycle.

Study Design: We developed a set of models that measure the long-term cost-effectiveness of 2 oncology drugs, paclitaxel and docetaxel, throughout their life cycles.

Methods: The study combined pricing history of the drugs, US Food and Drug Administration approval dates, drug utilization from Medicare claims, and clinical effectiveness information from phase III studies reported in the scientific literature. These data were used to estimate the incremental cost-effectiveness ratio (ICER) at the time of market entry and by year thereafter. The study population included patients with cancer who were treated with paclitaxel or docetaxel.

Results: The prices of paclitaxel and docetaxel dropped substantially due to patent expirations, while the number of users increased several fold because of subsequent empirical evidence and approval of new indications that resulted in greater efficacy. The ICER over a 10-year period was approximately 60% of the ICER at product launch for both drugs, and was further decreased when a longer-term perspective was taken.

Conclusions: We demonstrated that the ICER of a drug can decrease substantially over its life cycle. Thus, cost-effectiveness at drug launch might be a poor indicator of the longterm value of the drug. The results of this study are based on the analysis of 2 prominent oncology drugs, paclitaxel and docetaxel. The results may not be generalizable to other drug classes or other oncology drugs for which new indications are less common.

(Am J Manag Care. 2012;18:S249-S256)
Rapid increases in drug-related expenditures over the last several decades have led payers to focus increased attention on cost containment and the trade-off between the benefits of a drug and the drug’s budget impact. An increasingly common method to estimate this trade-off is to perform cost-effectiveness analysis (CEA).1 CEA provides a summary of value that is considered by payers to help inform their reimbursement policy for a drug.2 The use of CEA by payers also influences drug expenditures and pricing,1 which can subsequently have a significant impact on the pace and arrival of drug innovation.3-5 One important limitation of the current approach to conducting CEAs is that they are typically performed and attract the most attention at the time of initial drug launch or for individual indications; therefore, such analyses often disregard the overall cost-effectiveness of a single agent in the longer term, which can change dramatically over the life cycle of a drug.

The change in cost-effectiveness over time is especially important in the context of oncology drugs. The initial indication for a new oncology therapy is typically treatment of patients with late-stage disease for whom all approved treatment options have failed. It is challenging to demonstrate a large clinical benefit in patients with refractory disease who may have received multiple lines of therapy and may also have experienced toxicity from prior treatments. Subsequent indications tend to be for earlier lines of therapy, when patients are more robust and a greater clinical response is more likely. These populations also tend to be larger and, therefore, reflect a more common experience of patients treated with the drug. Therefore, CEA analyses focusing on the initial indication might not accurately capture the value of a drug over its life cycle for 2 important reasons. First, the patient populations receiving the drug can change substantially as new empirical evidence of the drug’s effectiveness is published or new indications for the drug are approved. A second and important factor is that the costs of the drug for payers also change significantly over time due to loss of exclusivity and patent expirations, even though many oncology agents, such as paclitaxel, remain important elements of standard of care well after they have lost exclusivity.

The cost-effectiveness of a drug at initial launch, then, might be a poor approximation of the drug’s cost-effectiveness over its life cycle. However, summary measures of a drug’s cost-effectiveness that include the increasing efficacy in subsequent indications and as price declines are rarely presented. To our knowledge, only 1 other article, by Garrison and Veenstra,6 has included computations of the cost-effectiveness of a drug from a dynamic or life-cycle perspective. To do this, the authors simulated the change in cost-effectiveness of trastuzumab from the time of drug launch due to the introduction of a new indication.

In this article we add to the literature by estimating the cost-effectiveness of 2 oncology drugs, paclitaxel and docetaxel, over their life cycles. These 2 drugs were chosen because they have played an important role in improving cancer survival and because we are able to observe changes in prices and utilization of these drugs from product launch to the present. We extended prior research along several dimensions. First, we used data on drug utilization and prices to show how cost-effectiveness changes over a drug’s life cycle from product launch to post–patent expiration. Second, we documented changes in cost-effectiveness due to new clinical evidence of efficacy, US Food and Drug Administration (FDA) approval of new indications, the size of the patient population treated, and changes in drug prices. Finally, we used Medicare claims data to construct a nationally representative sample to depict the ways in which the utilization of these drugs evolved over time by cancer type and year.

Methods

We developed the incremental cost-effectiveness ratios (ICERs) of 2 oncology drugs from a life-cycle perspective. These measures describe the net value of products over their life cycles rather than just at the time of product launch. The underlying model framework that we propose builds on the model developed by Garrison and Veenstra.6

To formally illustrate the difference between product life cycle and standard CEA measures, we first consider the ICER at product launch (t = 0), with the equation  in which IC0 is the incremental cost of the new drug at product launch and E0 is the measure of incremental effectiveness of the drug at product launch.

Next, we consider the ICER T time periods after launch:



In this equation, ICT and ET are the incremental cost and effectiveness of the drug T time periods after product launch, β is the time discount factor, and pT is the population of users T periods after product launch. Given the definitions above, the ICER T time periods after product launch is simply the ratio of the present value of incremental costs associated with the drug until period T (numerator) and the present value of the incremental effectiveness associated with the drug until period T. The ICER from a product life-cycle perspective is simply ICERT, where T equals the end of the product life cycle.

Because drugs can last on the market for several decades, we calculate ICERT for 10 and 20 years after product launch.

Notice that ICERT = ICER0 only if the following 2 conditions are satisfied:

1) the incremental costs do not change over the life cycle ICT = IC0, and

2) the incremental efficacy does not change over the life cycle ET = E0.

If either of the above conditions are violated, then ICERT ≠ ICER0. In this case, ICER0 would be a poor approximation of the true long-term cost-effectiveness of drugs.

As discussed earlier, the first condition is likely violated because drug prices can decrease dramatically over the life cycle, with prices falling to as low as 10% of the product launch price after patent expiration. Notice that the effects of reduction in prices after product launch will be magnified by the increased diffusion of the drug over time; in other words, lower prices mean that a larger population will use the drug. This would increase the weight attached to costs in later years, when prices are low, and would correspondingly decrease the weight attached to costs in earlier years, when prices are high.

The second condition is also likely violated as the efficacy and effectiveness of the drug might change over its life cycle. As mentioned above, this is especially likely for oncology products, which are initially used as second- or third-line treatment for patients with late-stage cancers; after product launch many oncology drugs receive approval for additional indications, such as treatment of early-stage disease and/or other types of cancers. As was the case with changes in price, the effects of changes in effectiveness will be magnified by the increased diffusion of the drug over time; in other words, increased effectiveness translates into greater diffusion of the drug. This would increase the weight attached to effectiveness in periods when efficacy is increased. To compute the ICER from a life cycle perspective we need information on the measures described below.

Measures

Incremental costs by indication. We estimated incremental costs by indication by using cost-effectiveness data published in peer-reviewed journal articles whenever possible. For some indications approved in the early 1990s, when costeffectiveness data were often not available, we estimated the incremental cost of treatment using information on unit prices and treatment regimens from published data and/or FDA product labels, including dosage and average number of courses.

Drug prices over the life cycle. To estimate the incremental costs for a given indication change over the life cycle of the drug due to changes in real drug prices, we obtained the wholesale acquisition cost (WAC) of paclitaxel and docetaxel in branded and generic forms (if applicable) from Analy$ourceOnline. These data were obtained from a national drug database, and are included with permission from and copyrighted by First DataBank, Inc. Information on the methodology of collecting price data is described at the First DataBank website: http://www.firstdatabank.com/Support/drug-pricing-policy.aspx. When multiple prices were posted for a single year, the lowest WAC available for that year was used. All costs and prices shown in this article are inflation-adjusted to 2010 US dollars, assuming an annual inflation rate of 3%.

Utilization rates by indication. For each drug we constructed nationally representative utilization rates by cancer type or indication from 1991 to 2007. The utilization data were derived from the surveillance, epidemiology, and end results (SEER) Medicare data and include the number of Medicare patients using each drug by major cancer categories, including breast, lung, prostate, and colorectal cancer. Because no utilization data are available for the time period after 2007, we assumed that utilization rates stayed at the 2007 levels for future years. The data were weighted to be nationally representative using information on national estimates of prevalence of different types of cancer by year. In particular, we assumed that the utilization rate of different drugs by cancer type is the same for Medicare and non-Medicare beneficiaries; therefore, national estimates of prevalence of different cancers multiplied by the utilization rates of different drugs by cancer type from SEER Medicare provide an estimate of the national utilization of drugs by cancer type.

Effectiveness. We also quantified life-cycle changes in efficacy. For each drug we identified the approval of new indications over the product life cycle from the FDA product labels, and the life-years gained for each new indication from peer-reviewed journal articles. We omitted certain indications (such as paclitaxel for second-line treatment of ovarian cancer and Kaposi’s sarcoma, and docetaxel for first-line treatment of non-small-cell lung cancer and gastric cancer), as no reliable data on cost-effectiveness were available or could be constructed for these indications. We combined these data with claims-based measures of the indication-specific utilization described above, in order to produce timevarying estimates of mean life-years gained. These estimates change with the introduction of new indications, as well as with shifts in the proportion of patients treated for newer or older indications over time.

Imputations in the Absence of Price and Utilization Data

 
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