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Understanding Price Growth in the Market for Targeted Oncology Therapies
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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.
Analytic Approach

We sought to determine whether each of our main outcomes (therapy price, number of patients, and annual revenues) was correlated with therapy launch year. To do this, we fit a series of regression models using the above measures as outcomes and therapy launch period as the key independent variable. We converted therapy launch year into a categorical variable with 3 groups: drugs launched between 1997 and 2002 (reference category), drugs launched between 2003 and 2009, and drugs launched between 2010 and 2015. We present results from 2 sets of regressions. In the first, we regress each of our outcomes of interest on this categorical variable corresponding to launch time period. In the second, we include an additional regressor (years post launch) to control for life cycle trends in the price and quantity of a drug following its market entry. We report trends in average regression-adjusted price and quantity and then report movement over time in the entire distribution of regression-adjusted annual revenues per therapy. We conducted sensitivity analyses involving (1) adding covariates to the base-case model, (2) using alternative values for key parameters related to the cost of R&D, and (3) asserting a linear relationship between launch year and outcomes, rather than the period fixed-effects structure described above. These are discussed in detail in the eAppendix.


Therapy Price

We begin with an analysis of the first study outcome, episode treatment price. The fact that newer oncology products are increasingly costly has been extensively documented in the literature.9,10 This trend is also readily apparent in our data on targeted agents. Figure 1 presents the results of a series of regressions that use therapy price as the dependent variable and launch year as the key independent variable and that follow the specifications defined previously.

We find a statistically significant positive correlation between price and launch year. The episode treatment cost for drugs launched between 2003 and 2009 was, on average, $23,000 greater than that for drugs launched between 1997 and 2002. The difference was even greater for drugs launched between 2010 and 2015—a statistically significant average difference in episode treatment cost of about $43,000.

Patient Count

This study sought to examine whether price growth has coincided with revenue growth. Thus, trends in annual average patient counts play a crucial role.

Figure 2 presents the results of the regressions for the patient count outcome, first without and then with the control for time since launch. Figure 2 demonstrates a strong negative relationship between launch year and average patient count. Relative to therapies launched in the early period, the dummy models suggest that therapies launched in the middle period were used by, on average, 28,000 to 35,000 fewer patients annually, whereas therapies launched in the late period were used by 33,000 to 44,000 fewer patients. Detailed time-series plots of patient counts for individual therapy–tumor pairs, and for average values within launch period, are presented in eAppendix Figures 1-4.

To confirm these results, we conducted a separate analysis of patient counts by therapy–tumor pair in the independent MCBS data set. Comparative results are presented in Figure 3.

Note that the IQVIA data set covers the entire US population, whereas MCBS covers Medicare patients only. Each is designed to be nationally representative for its particular sample frame.

There is clear evidence that annual patient populations are smaller for more recently launched drugs: In the main analysis using IQVIA data, the average patient count fell from 48,520 per drug for drugs launched in the early period to 4781 per drug for drugs launched in the late period, a decline of 90%. A decline of similar magnitude (85%) is observed in the Medicare data.

Annual Revenues

The reduction in quantity seems to have offset growth in price. The entire distribution of annual revenues has fallen over time. We use the regression-adjusted (ie, predicted) revenues from our regression model of revenues as a function of years since launch and time period. We also aggregate this up to the therapy level to eliminate the possibility that newer drugs spawn more indications and thus artificially lower revenues per tumor type. This permits uniform comparisons over time that account for the way in which revenue evolves over the life cycle of a drug. Figure 4 (A and B) presents the distribution of regression-adjusted annual revenues (at the therapy level) for each of the 3 launch periods; the difference between the 2 panels is that Figure 4B removes a single influential outlier—bevacizumab—from the data set. Both panels show that the distribution of regression-adjusted annual revenue has shifted left over time. In both cases, the most recent distribution ranges from $250 million to $500 million, whereas the earliest period shows a distribution from about $250 million to more than $900 million (all values are reported in 2015 US$). The sole difference between the distributions lies in the middle period. In the full sample, the main mass of the distribution lies between those of the early and late launch periods, but significant right skew is present. This long right tail is caused by the presence of a single drug, the blockbuster bevacizumab. Annual revenues for that drug (limited to the 6 indications of interest to this study) routinely exceeded $1 billion, in part because it was approved for more than 1 of those tumor types.

The Table presents the shifts in the distributions at key percentile points. Because bevacizumab is such an outlier, the Table accurately describes the distributions in both panels of Figure 4 (ie, the bevacizumab data points lie beyond the 90th percentile).

Annual adjusted revenues for the median drug have fallen from about $580 million to $287 million, a decline of about 50%. There is a decline of roughly 40% at the 25th percentile and nearly 60% at the 75th percentile. The only region of increase occurs at the 10th percentile, where revenues increased from the early to middle period, only to fall back down in the final period.

Results from the sensitivity analyses are presented and explained in the eAppendix.

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