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The American Journal of Managed Care June 2019
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Understanding Price Growth in the Market for Targeted Oncology Therapies
Jesse Sussell, PhD; Jacqueline Vanderpuye-Orgle, PhD; Diana Vania, MSc; Hans-Peter Goertz, MPH; and Darius Lakdawalla, PhD
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Understanding Price Growth in the Market for Targeted Oncology Therapies

Jesse Sussell, PhD; Jacqueline Vanderpuye-Orgle, PhD; Diana Vania, MSc; Hans-Peter Goertz, MPH; and Darius Lakdawalla, PhD
The prices of targeted oncology therapies have grown substantially, but revenues have not. This is due in part to large declines in per-drug patient counts.
ABSTRACT

Objectives: The causes of oncology drug price growth remain unclear. Analyzing corresponding trends in revenue can help understand these causes. This study seeks to assess changes over time in prices, patient counts, and drug-level revenues in the US market for oncology therapies and to investigate whether price growth is driven by an increased ability by pharmaceutical firms to capture profits.

Study Design: Nineteen-year retrospective study (1997-2015).

Methods: We used panel regression to investigate trends in prices, patient counts, and revenues within a US national data set consisting of targeted oncology therapies launched in different eras.

Results: We find that prices have roughly tripled, whereas average patient counts per therapy have fallen by 85% to 90% over this period. However, the entire distribution of annual revenues has fallen: For instance, median revenues for drugs launched in the early 2010s are about half of what they were for drugs launched in the late 1990s.

Conclusions: Future research on the causes of quantity decline can help inform pharmaceutical policy.

Am J Manag Care. 2019;25(6):273-277
Takeaway Points

Comparing targeted oncology therapies launched in the intervals 1997-2002, 2003-2009, and 2010-2015, we investigate trends over time in prices, the number of patients using each drug, and revenues within the US market.
  • We confirm the previously documented result that prices are rising over time.
  • However, revenues are not rising concurrently with prices.
  • This decline is attributable to contemporaneous declines of 85% to 90% in per-drug average patient counts.
Insurers, healthcare providers, and others have noted that prices for novel oncology treatments are rising over time.1-3 Price per episode of treatment has risen steadily, and prior research suggests that although the efficacy of cancer therapies (as measured by gains in overall survival) is certainly improving over time, prices are rising even faster.4 If improved efficacy or effectiveness does not provide the dominant explanation, then what else might be contributing to the observed increase in prices?

One possibility is that firms today possess greater market power and use it to earn greater rewards for their drugs. If true, higher prices would coincide with higher revenues. At first blush, the magnitude of price growth in the oncology market may make growth in revenues seem self-evident. However, it becomes more difficult to assess when one considers the fact that newer drugs tend to treat fewer patients, as we document later. This decline in patients treated may be due to greater personalization of therapy, slower incremental progress that fails to force older drugs out of the market, or other factors. Regardless, rising prices alongside falling quantities make the trend in revenues an empirical question.

An empirically interesting context for this investigation is the market for targeted oncology therapies, first introduced in the late 1990s. Prior research has documented that targeted therapies have quickly come to dominate the market: For example, targeted therapies accounted for about two-thirds of all chemotherapy expenditures by 2011.5 In this study, we investigate trends in prices, quantities, and revenues for targeted oncology therapies. We aim to determine whether higher prices have coincided with higher revenues and rewards for innovation or whether, instead, price growth has coincided with flat or falling revenues. Analyzing a sample of targeted therapies intended to treat common tumor types, we estimate the growth in price per patient-year, the reduction in the average size of the annual patient base using each drug, and the resulting change in revenue. In auxiliary analysis, we also estimate the implied increase in costs of drug development per patient-year.

METHODS AND DATA

We focused on targeted cancer drugs launched since 1997, when the first targeted agent (monoclonal antibody) was approved by the FDA. We considered all drugs primarily aimed at extending survival and/or progression-free survival for patients with cancer and focused on the 6 most common tumor types: breast, colorectal, melanoma, non-Hodgkin lymphoma, non–small cell lung, and prostate.6 As some drugs are indicated for more than 1 tumor type, the unit of analysis for this study was therapy–tumor pair. To account for the fact that many drugs have more than 1 indication, and that approvals for new indications are frequently granted subsequent to the initial approval, we computed estimates for all possible therapy–tumor pairs, including all indications known at the time of this analysis. We estimated relative usage of a single agent for different indications by assuming usage proportional to relative disease incidence. After we dropped data for indications not of interest, our final sample consisted of 29 therapies and 33 therapy–tumor pairs (see eAppendix Table 1 [eAppendix available at ajmc.com]).

Using data for each of these therapy–tumor pairs, we defined 3 measures of interest: therapy price, the number of patients using each drug for each indication, and annual revenues. We estimated each of these quantities for each therapy–tumor pair of interest, for each year in our analysis sample (1997-2015), and then used panel regression to investigate how they changed with respect to drug launch year. In supplementary analysis presented in the eAppendix, we also estimate trends in per-patient research and development (R&D) cost.

Therapy Price, Patient Count, and Revenues

We estimated the total number of patients and the estimated price of treatment using the IQVIA National Sales Perspective (NSP) data set.7 The NSP reports nationally representative estimates of the total annual units of individual therapies distributed in the United States and of the total related revenues received by manufacturing firms. We combined these data with monthly dose and average length of treatment values specific to each therapy–tumor pair (obtained from FDA labels and clinical trials) to derive our price and patient count outcomes. Price was calculated as the cost for a full course of treatment (ie, the total revenue estimated to return to the manufacturer as a result of 1 patient being treated). Patient counts were imputed through analysis of the total number of therapy units distributed in combination with label information on dosing for a full course of treatment. Revenues were calculated within-year as the product of price and patient count. Revenues were calculated at the therapy, rather than therapy–tumor, level because this captures the total return on launching a new drug. Complete details on the construction of these measures are provided in the eAppendix.

To validate our findings regarding trends over time in the average number of patients, we separately analyzed patient counts in an independent data set, the Medicare Current Beneficiary Survey (MCBS). The MCBS is a survey of a representative sample of Medicare beneficiaries and contains data on diagnosis and drug utilization.8 For each of the therapy–tumor pairs of interest, we counted the number of individuals in the MCBS sample who (1) had an indication for that tumor type and (2) reported using that therapy. We then used sampling weights to inflate these counts to the population level. We note that the MCBS results are estimates of counts for the Medicare population only and should not be construed as separate estimates for the aggregate US population. We repeated this process for the individual years between 1997 and 2012. (At the time of this analysis, 2012 was the most recent year for which MCBS data were available.) Finally, we compared trends over time in average patient counts as estimated in the IQVIA and MCBS analyses.


 
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