Long-term cost–effectiveness of oncotype dx<sup>®</sup> versus current clinical practice from a dutch cost perspective
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Long-term cost–effectiveness of Oncotype DX® versus current clinical practice from a Dutch cost perspective
J. Comp. Eff. Res.
Introduction: This study analyzes the incremental cost–effectiveness of
Michelle Kip1,2, Helma
Oncotype DX® testing to support adjuvant chemotherapy recommendations, versus
Monteban3 & Lotte Steuten*,1,4
current clinical practice, for patients with estrogen receptor-positive (ER+), node-
1Panaxea BV, Health Economics
negative or micrometastatic (pN1mic) early-stage breast cancer in The Netherlands.
& Reimbursement, Enschede,
Methods: Markov model projecting distant recurrence, survival, quality-adjusted
life years (QALYs) and healthcare costs over a 30-year time horizon.
Results:
2Department of Health Technology
Oncotype DX was projected to increase QALYs by 0.11 (0.07–0.58) and costs with
& Services Research, University of Twente, Enschede, The Netherlands
€1236 (range: -€142–€1236) resulting in an incremental cost–effectiveness ratio of
3Monteban Value Services, Amerongen,
€11,236/QALY under the most conservative scenario.
Conclusion: Reallocation of
adjuvant chemotherapy based on Oncotype DX testing is most likely a cost-effective
4Hutchinson Institute for Cancer
use of scarce resources, improving long-term survival and QALYs at marginal or
Outcomes Research (HICOR), Fred
lower costs.
Hutchinson Cancer Research Center, Seattle, WA, USA *Author for correspondence:
Approximately 14,000 women in The hand, this complexity is due to difficulties in Netherlands are diagnosed with breast can-
capturing disease heterogeneity, incorporat-
cer each year. Breast cancer remains the most ing tumor size, patient age and histology as common type of cancer in women and is risk factors [13–15]. On the other hand, adverse one of the most common causes of death for events related to chemotherapy treatment are women between the ages of 30 and 59 years experienced by almost all patients, varying in in The Netherlands [1,2]. Patients presenting severity from hair loss to death [13]. Besides with early-stage, estrogen receptor-positive the clinical pros and cons, the use of chemo-
(ER+) breast cancer preferentially benefit from therapy is associated with high costs [16,17]. adjuvant endocrine therapy, as demonstrated The high rate of chemotherapy prescription in large randomized controlled trials [3,4]. The in early-stage ER+ breast cancer patients indi-benefit from chemotherapy in terms of pre-
cates that a significant number of patients
venting or delaying distant recurrence after might undergo chemotherapy treatment primary surgery, however, is uncertain and despite a low recurrence risk, and therefore present in a small group of patients with this have only a limited possibility of achieving type of cancer. Despite this, current Dutch the health benefits. Consequently, there is a consensus guidelines, based on standard need for a predictive tool to help guide the clinical-pathological criteria, still recom-
decision to prescribe adjuvant chemotherapy,
mend adjuvant chemotherapy plus endocrine which may reduce both the burden and cost therapy for the majority of these patients [5]. of breast cancer management [15–17].
Consequently, a complex decision concerning
Recently, molecular assays to predict risk
this patient category is whether to prescribe and treatment response for early stage breast adjuvant chemotherapy treatment in addi-
cancer patients have become commercially
tion to endocrine therapy [6–12]. On the one available [18]. Of these, the Oncotype DX®
10.2217/CER.15.18 2015 Future Medicine Ltd
J. Comp. Eff. Res. (Epub ahead of print)
Research Article Kip, Monteban & Steuten
assay has shown to provide both predictive and prog-
more likely to follow the treatment suggested by Onco-
nostic information, beyond traditional parameters, in type DX than low RS patients (RR: 1.07 [95% CI: node-negative (N0) and node-positive (N+) hormone 1.01−1.14]). While the clinical and patient benefits of receptor-positive disease [19–23]. This assay predicts both Oncotype DX are compelling, these have to be balanced the 10-year recurrence risk of breast cancer as well as against the costs of the Oncotype DX assay (€3180), and the expected benefit of adjuvant chemotherapy treat-
the potential downstream cost impacts. This study ana-
ment [2]. The recurrence score (RS) is expressed in a lyzed the incremental cost–effectiveness of Oncotype
numeric value between zero and 100 using a proprietary DX testing to support adjuvant chemotherapy decision-
algorithm. This score is used to stratify patients in three making versus current clinical practice in treatment of
risk groups: low, intermediate and high risk of disease patients with ER+, node-negative or micrometastatic
relapse [24]. The correlation between the Oncotype DX (pN1 mic) early-stage breast cancer in The Netherlands.
Recurrence Score (RS) and both distant recurrence and/
or survival, and the benefit from chemotherapy, has been
Methods
reported by Paik
et al. in two prospective analyses of Analytical framework
archived tissue from randomized clinical trials (NSABP A Markov model was developed using Microsoft Excel
B-14 and B-20) [19,20]. Results of this analysis indicated (Redmond, WA, USA) to model the recursive disease
that Oncotype DX independently predicts the 10-year processes, including the annual risk of recurrence and
recurrence risk of breast cancer, as well as the response mortality. The model is based on the original model
to adjuvant chemotherapy treatment [17,20]. Recently, structure by Hornberger
et al. [18], which was initially
Carlson
et al. (2013) reported that, based on a meta-
developed for a NICE submission in England and
analysis of 21 studies, Oncotype DX changed overall Wales and has been adapted to the Dutch setting, in treatment recommendations in 33.4% of patients [25]. accordance with the applicable pharmacoeconomic They also found that high RS patients were significantly guidelines [26]. The analysis was performed from a
Oncotype DX® testing
Figure 1. Overview of model structure. ESBC: Early-stage breast cancer.
J. Comp. Eff. Res. (Epub ahead of print)
future science group
Cost–effectiveness of Oncotype DX® from a Dutch cost perspective Research Article
Table 1. Summary of clinical input parameters.
SE (minimum;
Study (year)
Registration (2013)
Net change in chemotherapy use -18.60
1.86 (-20.50; -9.30)
2.10 (-20.50; -10.48)
2.00 (-20.50; -10.00)
1.87 (-20.50; -9.34)
Net change in chemotherapy use 7.5
0.75 (3.75; 11.25)
with intermediate RS (%)
0.19 (0.95; 2.86)
0.05 (-0.75; -0.25)
0.32 (-4.82; -1.61)
Net change in chemotherapy use 1.90
0.19 (0.95; 2.85)
0.48 (2.38; 7.14)
0.16 (0.80; 2.40)
0.44 (2.21; 6.63)
10-year risk of recurrence (low
1.60 (1.60; 4.80)
RS) on endocrine therapy (%)10-year risk of recurrence
4.30 (4.55; 13.65)
(intermediate RS) on endocrine therapy10-year risk of recurrence (high
7.30 (19.75; 59.25)
RS) on endocrine therapy (%)RRR with chemotherapy
N/A (0.00; 20.00)
Assumption based
RRR for chemotherapy
4.43 (19.5; 58.5)
(intermediate RS) (%)RRR for chemotherapy
3.95 (37.0; 100.0)
(high RS) (%)Postrecurrence survival (years)
0.330 (1.65; 4.95)
Central Bureau of
Statistics of The Netherlands (2012)
HT: Hormone/endocrine therapy; N/A: Not applicable; RRR: Relative risk reduction; RS: Recurrence Score; SE: Standard error.
Dutch healthcare payer's perspective comparing the in the model start in the ‘recurrence-free' state, and clinical outcomes and costs of ‘Oncotype DX testing' in each 1-year cycle of the simulation, patients might to ‘current clinical practice' involving clinical–patho-
experience either a recurrence or mortality event, and
logical assessment, over a time horizon of 30 years. might therefore transition to either the ‘recurrence' or The model made projections of life expectancy, qual-
the ‘dead' state (absorbing state). Transition probabili-
ity-adjusted life expectancy and direct costs, based on ties between the three health states are based on Horn-recurrence rates for low-, intermediate- and high-risk berger
et al. [27]. To avoid systematic over- or underes-patients as well as country-specific mortality data. The timation of survival in the model, half-cycle correction model structure is outlined in Figure 1.
was performed.
Three Markov health states are defined to estimate
the cumulative costs and QALYs based on hypothetical Patient population & comparatorscohorts of 100 women with ER+, node-negative, or sin-
Starting age of the cohort was 60 years, which is the
gle node positive early-stage breast cancer. All patients median age of incident breast cancer cases as reported
future science group
Research Article Kip, Monteban & Steuten
Table 2. Chemotherapy costs and adverse events.
50% patients 3× FEC 3× docetaxel treatment
Mean 180 mg per patient per session (derived from
50% patients 6× TAC treatment regimen
Taxotere/doxetaxel = 1.8 × 75 = 135 mg per session per
patientAdriamycine/doxorubicine = 1.8 × 50 = 90 mg per session per patient
G-CSF use with docetaxel
G-CSF use with TAC
Add-on DRG for docetaxel
€4.07 per mg [36]
Add-on DRG for doxorubicine
€23.56 per mg [36]
DRG for intravenous/intrathecal chemotherapy in 95% of patients (derived from expert), average tariff nonmetastatic tumors, nonclinical
of €1225.64 as derived from publicly available data of 73 Dutch hospitals. Six DRGs per chemotherapy
DRG for intravenous/intrathecal chemotherapy in 5% of patients (derived from expert), average tariff nonmetastatic tumors, with side effects requiring of €4409.91 as derived from publicly available data of hospitalization
73 Dutch hospitals. Six DRGs per chemotherapy
DRG: Diagnosis-Related group; FEC: Fluorouracil, epirubicin, cyclophosphamide; TAC: Docetaxel, adriamycine, cyclophosphamide.
by The Netherlands Cancer Registration [28]. In cur-
zero. All recurrence rates are assumed to be indepen-
rent clinical practice, patients receive endocrine therapy dent of age. Nonbreast cancer death was captured as a and 3rd-generation chemotherapy regimens according competing risk in the model. These data were derived to Adjuvant! Online and the Nottingham Prognostic from The Netherlands Cancer Registration and based Index. Therapy recommendations in current clinical on Dutch female life tables for 2007–2009 [28,33]. For practice for low, intermediate and high risk were based patients experiencing distant recurrence, survival was on the study of Albanell
et al. [29]. The net change assumed to be 3.3 years [32]. Long-term adverse events in the percentage of chemotherapy (CT) use follow-
associated with chemotherapy are not captured in the
ing Oncotype DX testing was also extracted from model (including cardiotoxicity, leukemia, and mild Albanell
et al. for the base case analysis. In addition, cognitive impairment).
we modeled the net change in % CT use as reported in a German cohort (Eiermann
et al. [30]), in a British Costscohort (Holt
et al. [31]) and based on additional data The Markov model incorporates cost of endocrine from the Carlson
et al. [25] meta-analysis as provided therapy, cost of chemotherapy and cost of distant by the authors. Data input for the base case (Albanell) recurrence. Cost of endocrine and chemotherapy, and and alternative analyses (Eiermann, Holt and Carlson) cost of accompanying adverse events, were derived are specified in Table 1 [25,29–31].
from tariffs of The Netherlands Healthcare Authority (NZA) and expressed in Euros [34]. Costs of endocrine
therapy were derived from the NZA and from the
Data on recurrence risks associated with endocrine official website of the National Health Care Institute, therapy and relative risk reduction associated with listing medication costs [35]. Endocrine treatment chemotherapy, were derived from the NSABP B-20 costs are incurred over 8 years, with higher annual cohort. These data were evaluated for each simulated costs in years 1–5 and lower costs in years 6–8, reflect-patient in each cycle of the model, based on their RS ing change in treatment intensity over time. One (low, intermediate, or high), as shown in Table 1 [20]. hundred percent therapy compliance was assumed. These risks were adjusted based on whether patients The treatment cost of the two most commonly used were receiving chemotherapy as per the initial recom-
third-generation CT regimens were applied assum-
mendation (in the current clinical practice arm) and ing a 50–50 distribution among all patients recom-based on the RS (in the Oncotype DX arm). Relative mended for CT, as based on communication from risk reduction of distant recurrence among low risk physicians of the Leiden University Medical Center in patients receiving chemotherapy was assumed to be The Netherlands. The first regimen involves six cycles
J. Comp. Eff. Res. (Epub ahead of print)
future science group
Cost–effectiveness of Oncotype DX® from a Dutch cost perspective Research Article
of TAC (T = docetaxel, A = adriamycine, C = cyclo-
rence. Thomas
et al. stated that the population cur-
phosphamide), combined with primary G-CSF ther-
rently under consideration (ER+ and HER2-), incurred
apy. The second one involves three cycles of FE100C lower treatment costs than ER+ and HER+ patients. (5-fluorouracil 500 mg/m2, epirubicin 100 mg/m2, While their estimation of mean cost across all groups cyclophosphamide 500 mg/m2), and three cycles of may thus be an overestimate for this population, it is docetaxel. For those patients receiving chemotherapy, still lower than that reported elsewhere [38]. Follow-the costs of chemotherapy and endocrine therapy are ing Dutch pharmacoeconomic guidelines, future costs both incurred in the first year, indicating an overlap were discounted at 4% and clinical benefits at 1.5% of costs of approximately 3.5 months. Costs of che-
annually. A summary of the cost variables used is
motherapy and G-CSF are included in the relevant provided in Table 3.
diagnosis related group (DRG) tariff, which further includes all in- and out-patient activities that a hospi-
tal performs following an ICD-10 diagnosis as well as Health utility values were obtained from published lit-the honorarium for the medical specialists involved. erature and were used to calculate the quality-adjusted Some medications are not included in the DRGs and, life-years (QALYs). These scores are quantitative rep-therefore, considered as DRG add-ons. In this case, resentations of the desirability of a particular health costs of docetaxel and doxorubicin are add-ons to outcome, with a utility of 0 being equivalent to death the DRG, and costs have been derived for an average and a value of 1 equaling perfect health. Health-related patient (135 mg docetaxel, 90 mg doxorubicin) [36]. quality of life associated with recurrence is taken from A summary of all costs associated with chemotherapy Milne, who reported an analysis in New Zealand costs, its adverse events and the corresponding data women with advanced breast cancer, and assumed treat-sources is provided in Table 2.
ment via endocrine therapy [39]. In addition, a disutility
Other drug costs were derived from the Pharma-
associated with chemotherapy of 0.07 was applied to
cotherapeutic Compass [37]. Adverse events associ-
capture the health-related quality of life impact of che-
ated with endocrine therapy are effectively set to zero, motherapy in the first model cycle (for those patients because these costs are incorporated in the DRG tariff. recommended chemotherapy in each treatment arm), Costs of distant recurrence were derived from a study and this value was taken from Peasgood
et al. [40]. The by Thomas
et al. including active, supportive and end health utility associated with one year in the recurrence of life care, and recalculated to 2012 Euros [32]. Total free state was assumed to be the same during and after cost of recurrence is incurred at the time of recur-
endocrine therapy (Connor-Spady
et al.) [41]. The util-
Table 3. Summary of cost variables in the cost–effectiveness modeling analysis.
Mean cost (€) SE
Oncotype DX® test
Genomic Health (2014 list
Endocrine therapy (years 1–5) 877.71
DRG tariff derived from the
NZa and medicijnkosten.
Endocrine therapy (years 6–8) 114.27
DRG tariff derived from the
NZa and medicijnkosten.
DRG tariff derived from the
NZa and Pharmacotherapeutic
Distant recurrence (monthly)
Thomas
et al. (2009) [32]
Endocrine therapy adverse
Not taken into account,
events (years 1–5)
Endocrine therapy adverse
Not taken into account,
events (years 6–8)
Chemotherapy adverse events 0.01
Not taken into account,
SE: Standard error.
future science group
Research Article Kip, Monteban & Steuten
Table 4. Utility scores used in the modeling analysis.
State or event
Mean utility
Distribution Study (year)
1 year in recurrence-free state
Conner-Spady (2005)
1 year in recurrence state
Chemotherapy treatment (six cycles) -0.07
SE: Standard error.
ity scores used in the modeling analysis are reported minimum values are assumed at ± 50% of the mean. in Table 4.
These assumptions were considered conservative since they are likely to exceed the true variance. The appro-
Sensitivity analyses
priate number of runs to be included in the PSA was
One-way and probabilistic sensitivity analyses (PSA) estimated based on the mean ICER plotted against the were conducted. In the one-way sensitivity analy-
number of iterations of the PSA. Variation in sampling
sis, key drivers of model outcome were identified was found to be sufficiently minimized when running by varying each of the individual input parameters the PSA 1000 times.
over its predetermined range. Both costs and clini-
The model was validated by a third party health eco-
cal parameters were varied, and the impact of each nomic modeler, who reviewed both the face-validity of on the incremental cost–effectiveness ratio (ICER) the model structure and the formulas used. No further was noted. The impact of five (
a priori determined) sensitivity analyses were performed to investigate struc-key-parameters will be shown in a tornado diagram, tural uncertainty. An internal validation exercise was namely the mean starting age of the patient cohort, performed, to compare model outcomes against the the cost of Oncotype DX, the net change in chemo-
study on which it was based. Results from the NICE
therapy use following Oncotype DX testing, the ten model and the validation exercise were found to closely
year risk of recurrence with hormonal therapy, the match in both treatment arms (data not shown).
relative risk reduction (RRR) of recurrence with use
of chemotherapy. For the latter three parameters, the
Results
sensitivity is evaluated for low, intermediate and high Oncotype DX testing was associated with a notable
risk separately.
change in treatment recommendations, of which our
The joint decision uncertainty surrounding the base case reflects the most conservative scenario with
model outcomes was quantified by means of a proba-
18.6% less CT use in low risk patients, and 7.5%
bilistic sensitivity analysis (PSA), which employs ran-
and 1.8% more CT use in intermediate and high
dom draws from predetermined parameter distribu-
risk patients, respectively (Table 5). The data from
tions that were defined using the method-of-moments Eiermann
et al. reflect the scenario with the highest approach [42]. Where reported, distribution parameters impact of Oncotype DX on the net change in CT use, (such as standard error [SE] and minimum and maxi-
with -20, -0.5 and +1.6% for low-, intermediate- and
mum values) were taken from literature. When insuf-
high-risk patients, respectively [30].
ficient information was available, the standard error
A scatter plot presenting the base case results from
was assumed to be 10% of the mean; maximum and the PSA is shown in Figure 2, depicting the incremen-
Table 5. Summary of changes in adjuvant therapy recommendations with Oncotype DX®.
Recurrence score
Post Oncotype® DX
Net change CT (%)
ET + CT (%)
ET + CT (%)
CT: Chemotherapy; ET: Endocrine therapy.
Data taken from [29].
J. Comp. Eff. Res. (Epub ahead of print)
future science group
Cost–effectiveness of Oncotype DX® from a Dutch cost perspective Research Article
tal costs and QALYs for a cohort of 100 patients. The tive compared with current clinical practice in The mean difference in quality-adjusted life expectancy for Netherlands. Oncotype DX testing was associated Oncotype DX versus current clinical practice is 0.11 with a notable change in treatment recommenda-QALYs per patient, and comes at an incremental cost tions based on the data reported in three different of €1236 per patient, yielding an ICER of €11,236 per original studies and a meta-analysis. Based on these QALY gained. A cost–effectiveness acceptability curve changes, and modeling the long-term risk of distant was plotted based on the findings of the PSA with will-
recurrence based on published trial data, Oncotype
ingness to pay thresholds ranging from €0 per QALY DX was projected to improve quality-adjusted life gained to €40,000 per QALY gained. At a willingness expectancy per patient with 0.11 QALYs and 0.13 life to pay threshold of €30,000 per QALY there is a 99.5% years in the base case analysis, which is also the most probability that Oncotype DX is cost effective versus conservative.
current clinical practice (data not shown).
PSA showed that there was a 99.5% probability that
Table 6 shows per patient costs, QALYs, life years Oncotype DX would be cost effective, at a willingness
(LYs) and ICERs for the base case and the alterna-
to pay threshold of €30,000 per QALY gained. One-way
tive analyses. Incremental costs are negative in the sensitivity analyses showed that the cost–effectiveness analysis based on Eiermann
et al. data [30] (-€124 per of Oncotype DX testing was most sensitive to varia-patient) and positive in the three other analyses (range: tions in patient age, cost of the Oncotype DX assay and
€325–1236). In all analyses, incremental QALYs and net changes in chemotherapy use in low risk patients. LYs are higher for the Oncotype DX versus the current Regarding the latter, it is notable that the studies under-clinical practice strategy, varying from +0.07 QALYs pinning this model are very consistent regarding the and +0.07 LYs (Eiermann
et al. [30]) to 0.58 QALYs percentage change in treatment recommendation in and +0.73 LYs (Holt
et al. [31]). The ICERs range the total patient group (varying between 27% [31] and from +€11.236/QALY and +€9.508/LY gained 33% [25,30]), but less so regarding the net change in che-(based on Albanell
et al. [29]) to dominance (based on motherapy in the intermediate- and high-risk groups. Eiermann
et al. [30]).
While we cannot provide a full explanation for this
The tornado diagram in Figure 3 shows that varying based on the reported data, we observe that a positive
the starting age of the patient cohort has the highest net change in chemotherapy in the intermediate-risk impact on the model outcome, followed by the cost group [25,30], and a higher positive net change in the of the Oncotype DX assay, and the net change in high-risk group [25,31], coincides with a relatively higher chemotherapy use following Oncotype DX testing.
proportion of patients classified in each of these groups. That said, in all sensitivity analyses, Oncotype DX was
Discussion & conclusion
associated with ICERs in the range (€20,000–€80,000)
This cost–effectiveness analysis contributes to the that would be considered cost effective by commonly evidence that Oncotype DX is likely to be cost effec-
quoted standards in The Netherlands.
Cost (€
Figure 2. Incremental cost–effectiveness scatterplot. CE: Cost–effectiveness; ICER: Incremental cost–effectiveness ratio; QALY: Quality-adjusted life year.
future science group
Research Article Kip, Monteban & Steuten
Table 6. Summary of cost–effectiveness results for the four studies.
Current clinical Oncotype
DX® testing
Quality-adjusted life expectancy (QALYs) Albanell
Life expectancy (years)
ICER (cost per QALY gained)
ICER (cost per life year gained)
Values shown are per patient.
ICER: Incremental cost–effectiveness ratio; QALY: Quality-adjusted life year.
Data taken from [25,29–31].
The cost–effectiveness analysis can be considered tions have been conservative (overestimating the likely
conservative in some regards. First, this analysis did variance). Although sensitivity analyses have not been not capture some of the long-term adverse effects of performed to investigate the impact of changing these chemotherapy treatment (including cardiotoxicity, sec-
assumptions, it is unlikely that any changes within
ondary leukemia or effects on cognitive impairment). plausible limits would notably alter the findings of Local recurrence was not captured in the modeling the analysis, although specific results and estimates of analysis and, given the purported benefits in terms of uncertainty may be influenced.
treatment recommendations with Oncotype DX test-
Although recommending a similar study in the
ing, this may also have led to an underestimation of the Dutch setting, comparing Oncotype DX versus current clinical benefit of Oncotype DX.
clinical practice, might seem obvious, the added value
A potential weakness of the analysis is that, in of such research is questionable. First of all, the results
absence of Dutch data sources, it had to rely on long-
of this analysis were generally consistent with those
term clinical data from the USA to estimate the risk of observed in multiple other countries [43,44], with dif-distant recurrence. Also, health-related quality of life ferent clinical practices and other cost levels. Second, utility scores were not Dutch-specific. Ideally, clini-
the high costs associated with performing those stud-
cal data from a Dutch setting addressing both of these ies, and the inevitable burden to patients for participat-aspects would have been available to support the mod-
ing in a study, are serious considerations to be made.
eling analysis. Furthermore, information on variance Although no formal Value of Information analysis has around many of the model inputs was limited and, been performed, the current analysis shows little deci-therefore, assumptions have been made regarding the sion uncertainty and the one-way sensitivity analyses shape and, in certain cases, the parameters defining indicate that other values for key-inputs are unlikely distributions for PSA. In most cases, these assump-
to change the overall recommendation. Instead, with
J. Comp. Eff. Res. (Epub ahead of print)
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Cost–effectiveness of Oncotype DX® from a Dutch cost perspective Research Article
more prognostic tests coming to the market nationally come the challenges mentioned above. Yet, in order to and internationally, for example, the Mammaprint®, base pricing on value, it is necessary that the definition MammaTyper®, PAM50, Endopredict and others [45], of value also captures the benefits of diagnostics appro-head-to-head studies regarding their impact on treat-
priately [48]. The implementation of pricing flexibility
ment decisions would provide important and timely could be facilitated by risk-sharing schemes, such as information for appropriate, value-based, adoption Oncotype DX and the US payer UnitedHealthcare and reimbursement decisions. Furthermore, while the agreed to, whereby they collected information on Oncotype DX is furthest along the validation pathway whether women actually forego chemotherapy when compared with the other genetic tests mentioned above, recommended to do so by the diagnostic.
the need remains for evaluating the accuracy of the
Finally, diagnostic reimbursement is in many coun-
results and predictions of the proprietary algorithms of tries, including The Netherlands, not always included these tests before definitive conclusions can be drawn in the cost of care for outpatients, and is commonly on any of them. Prospective studies are needed to do spread across multiple siloed budgets. This means that so, while longitudinal (observational) studies can help a test developer may face high barriers to getting a test to understand the relationship between predicted risk adopted because of budget constraints and costs falling and observed risk and hence its real-word impact on on other budget silos than those where the gains are clinical outcomes [46].
enjoyed, even when it has good evidence of cost–effec-
When recommending more research, urgent atten-
tiveness [49]. Detailed budget impact analyses, which
tion is also required to the situation that payments for project the potential financial impact of the adoption diagnostics do not reflect the cost of evidence genera-
and diffusion of a new technology into a healthcare
tion or the value created [47]. Currently, payments to system with finite resources, are a means to generate drug manufacturers are typically based on projections insight in which budget holder is paying for the diag-of likely impacts on QALYs and costs, while diagnos-
nostic and which one absorbs the downstream cost sav-
tic reimbursement tends to be based on cost only. As ings [50]. Potential disincentives can herewith be identi-a consequence, neither the full benefits nor costs of fied and when budget holders are (held) accountable effective stratification are fully reflected in the pricing they could use this information to align the incentives and reimbursement systems for drugs and diagnostics and find a way to adopt a highly cost-effective diagnos-in most EU healthcare systems including The Nether-
tic. In The Netherlands, health insurers are particularly
lands. A system of flexible pricing has been proposed by well positioned to play a leading role in this, as they the Academy of Medical Sciences (2013) that is based negotiate prices for each Diagnostic Related Group on value both for drugs and for diagnostics, to over-
with hospitals and primary care groups on an annual
ICER (cost per QALY)
Net change in CT use following Oncotype DX® testing (low)
Net change in CT use following Oncotype DX testing (intermediate)
Net change in CT use following Oncotype DX testing (high)
10-year risk of recurrence with HT (low)
10-year risk of recurrence with HT (intermediate)
10-year risk of recurrence with HT (high)
RRR of recurrence with use of CT (low)
RRR of recurrence with use of CT (intermediate)
RRR of recurrence with use of CT (high)
Mean starting age of patient cohort
Figure 3. Tornado diagram of one-way sensitivity analyses. CT: Chemotherapy; HT: Hormone/endocrine therapy; ICER: Incremental cost–effectiveness ratio; QALY: Quality-adjusted life year;
RRR: Relative risk reduction.
future science group
Research Article Kip, Monteban & Steuten
basis. Given they are one of the major beneficiaries of a flexible approach to evidence generation, which bal-the significant cost savings due to expensive and inef-
ances the need for high-quality evidence with appro-
fective chemotherapies avoided by diagnostics such as priate incentives for innovation, will receive more Oncotype DX, they have generally been unresponsive attention in the future. Adaptive or observational tri-as to play a constructive role in this debate.
als designs and the application of advanced analytics to real-world data may well be the way forward, and
there is large potential for big data analytics to sup-
The Oncotype DX test is currently considered for port such comparative effectiveness studies. As such, reimbursement in The Netherlands and other EU Health Technology Assessment bodies, regulators countries. In the USA, it is already included in major and payers must (re-)consider not only the evidence treatment guidelines for breast cancer and it receives a requirements for informed decision-making, but also value-based reimbursement, which is based on clinical the consequences of such demands to healthcare data demonstrating the test's ability to restrict health-
innovation, sustainability and population health.
care costs. The test is often considered the poster child for other genomic tests that are not linked to one spe-
cific drug and herewith encounter the typical drug-
The authors grateful y acknowledge Dr Roth of the Fred
diagnostic co-development model. Notwithstanding Hutchinson Cancer Research Institute and Dr Carlson of the the appeal of such tests, most countries, except the University of Washington (Seattle, WA, USA) to share the UK, do not currently have a framework for calculat-
crude data of their previously published systematic review
ing the added value of stratification and separating the and meta-analysis [25] for reanalysis in this cost–effectiveness rewards for this between the therapeutic and diagnos-
tic part [47]. Another current challenge is generating the evidence base for the clinical utility and predictive Disclaimervalue of these tests. While Oncotype DX and some All views expressed in this paper are the author's and the other genetic assays have been shown to effectively author's only.
guide treatment decisions, the use of such prognos-tic tests is not necessarily predictive, and long-term Financial & competing interests disclosurepatient outcomes are still unknown. Generating such Monteban Value Services b.v. and PANAXEA b.v. received evidence, requires large prospective studies that take funding from Genomic Health Inc. for developing and ana-time and substantial investments (from often fairly lyzing the model for The Netherlands. The authors have no small diagnostics companies) to be performed. Given other relevant affiliations or financial involvement with any the speed at which technology as well as usual care organization or entity with a financial interest in or financial changes for this sector, a strict insistence on random-
conflict with the subject matter or materials discussed in the
ized control trials, for instance, is increasingly unfea-
manuscript apart from those disclosed.
sible and likely leaves a new diagnostic outdated by
No writing assistance was utilized in the production of this
the time premarket evidence is generated. Therefore, manuscript.
Executive summary
Rationale
• Oncotype DX® is likely to be cost effective compared with current clinical practice in The Netherlands.
• The assay is expected to improve survival and quality-adjusted life expectancy.
Findings
• The assay comes at an incremental costs of €1236 per patient, yielding an ICER of €11,236 per QALY gained.
• The cost–effectiveness of testing is most sensitive to variations in patient age, assay cost and net changes in
chemotherapy use in low-risk patients.
• The results of this analysis were consistent with those observed in multiple other countries.
Discussion
• Even cost-effective diagnostics face high barriers to adoption and reimbursement because their costs and
benefits fall on separate budget silos.
• Currently, neither the full benefits nor costs of effective patient stratification are fully reflected in the pricing
and reimbursement systems for drugs and diagnostics.
Recommendation
• Future studies should focus on head-to-head comparisons of various assays to provide timely information for
adoption and reimbursement decisions.
J. Comp. Eff. Res. (Epub ahead of print)
future science group
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Source: https://www.panaxea.eu/wp-content/uploads/2015/04/Kip-et-al.-JCompEffRes04152015epub1.pdf
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