4. Discussion
At present, the cost comparative between NAC and AC for lung cancer
patients has not been extensively studied. We searched the PubMed
database to identify cost-effectiveness analyses of NAC and AC in NSCLC,
published before January 2020. Tree searches were done, with the
following search terms: adjuvant chemotherapy and neoadjuvant
chemotherapy, cost effectiveness; cost effectiveness, adjuvant
chemotherapy, neoadjuvant chemotherapy and lung cancer; cost
effectiveness, preoperative, postoperative, chemotherapy and lung
cancer. There are 7 studies that had evaluated the cost-effectiveness of
NAC and AC. But none of the studies had assessed the cost-effectiveness
specific to lung cancer. Off the 7 cases mentioned, 6 are about ovarian
cancer and 1 is about head and neck cancer [14]-[19].
Among those research studies, the study in head and neck cancer showed
that NAC is more cost-effective than AC [19]. 4 of the
studies[14]-[17] about ovarian cancer showed the same result and
two studies [18][19] showed that AC as being dominated strategy.
Findings from the previous research studies (which stated that the
therapeutic regimen is more cost-effective) do not remain constant. Our
study showed that NAC is more cost-effective than AC, with a cost saving
of ¥618.90 and a QALY increment of 0.10 years per patients.
In contrast to those previous studies, the input parameters in our
model, included the cost of chemotherapy adverse events, Among those
research studies, only one [14]
explicitly incorporated the
chemotherapy adverse event into the model. The reason for this maybe
that there were no significant
differences in the chemotherapy-related toxicities for NAC and AC in
ovarian cancer and head & neck cancer [15][31]. For NSCLC
patients however, the tolerability of chemotherapy and the ratio of AE
are significantly different in NAC and AC as supported by the NATCH
phase 3 trial [3] and the study by Brant et al [10].
Nonetheless, the difference in tolerability of chemotherapy and the
ratio of AE does not contribute to the OS. Besides this, the treatment
expense of 3 and 4 grade AE is even higher than the surgery procedure
cost [14]. Thus, although the result was not sensitive to the ratio
and cost of AE in our model, we think cost comparison between NAC and AC
needs to consider the impact of AE.
In our study, the sample population is cT2-4N0-1 NSCLC patients,
excluding stage Ⅳ patients (for whom NCCN guidelines recommend two
treatment strategies). The choice of NAC and AC is a tough one in the
initial treatment phase. The patients who are less clinically at-risk,
benefit more from AC, while the stage Ⅳ patients are recommended
systemic therapy by NCCN and there is robust evidence in support of the
same [2]. Thus, our study focused on the sample population of
patients whose treatment strategies were controversial.
However, most studies compared NAC or AC with the treatment of surgery
alone, and estimated survival benefit. Very few studies directly
compared the two chemotherapy approaches [7][8]. The head to
head comparison of the studies of NATCH and Brandt et al in light of NAC
and AC, showed that there are no statistically significant differences
in the OS and DFS. But, the NATCH trial was criticized for being overly
optimistic and over representing the study design [7][8]. The
percentage of stage Ⅰ disease patients who did not benefit from
chemotherapy in the study cohort is 75%. In comparison with the
meta-analysis [12], the stage Ⅰ disease patients in NAC cohorts
account for nearly 50%. This is the reason base case probabilities are
based on the study of Brandt et al in our model.
Beside this, our study was based on real-world data. The study generated
two groups (92 in NAC and 92 in AC) with comparable characteristics
through strict exclusion criteria and propensity score matching
analyses, to prevent selection bias related to nonrandomized cohort. The
ratio of males and females more closely reflects the real-word
population of NSCLC patients who need to receive either NAC or AC.
What’s more, the study sample population excluded the patients with
microscopic and macroscopic residual disease (R1/R2 resection), which
avoids the influence of surgery discrepancy (since the surgery which
results in resection to minimal or no gross residual disease may be
associated with a long-term survival advantage). The single-center data
source reduced the effectiveness of surgery.
There are some limitations to our model. As with all cost-effectiveness
analyses, assumptions in clinical base cases, cost and quality of life
are important to the projected outcomes determined by the model.
Consequently, 1-way and probability sensitivity analyses were performed
to test our assumptions. The sensitivity analyses showed that our model
was robust enough to handle to the variation of cost, quality of life,
ratio of complication and AE. But the variation of OS would change the
conclusion of the cost-effectiveness analysis in our model.
The medial OS is the most sensitive parameter in our cost-effectiveness
analysis model. The study of Brandt et al, NATCH trial and Tim et al all
show that the medial OS of NAC and AC have no significant
difference[3][10][12][13]. In fact, the difference
(<0.12 years) of NAC’s and AC’s medial OS (9.22 VS 8.98 year
in Brandt et al) is enough to change the conclusion of our model. Using
9.22 and 8.98 years as the OS of NAC and AC in our model, NAC is more
cost effective with the ICRE of 3070 RMB/QALY. Given the concern of
survival in lung cancer treatment for NSCLC patients, it is important to
evaluate sensitivity of OS in cost-effectiveness analysis.
Simultaneously, there are several assumptions in the cost. To make the
model clear and accurate, our cost measures were
intentionally confined to the associated costs of initial treatment
phase. This was also based on the assumption that there would not be a
significant difference between treatment and ongoing care in the NAC and
AC groups beyond the initial recovery period. However, if long term
surgery complication or chemotherapy adverse events affected one group
and increased the follow-up medical treatment, the difference of NAC and
AC cost may be improperly over
underestimated. Beside this,
patients need to do more imaging examination in the NAC treatment,
and one patient did not only have
once AE in the chemotherapy treatment.
In addition, probabilities used in estimating surgery complication and
postoperative death, may be overrated in NAC, because patients with more
comorbidities or more complex diseases may be more likely to receive
NAC. That is why the ratio of related complication in NAC is higher than
AC in our model. In the NATCH trial however, the postoperative death of
AC is higher than NAC (5% VS 7.5%) and the ratio of complication in
the multicenter randomized controlled trial (CRT) is influenced by the
level of the surgery team. In the case of chemotherapy tolerance, our
model did not consider the probability of completed chemotherapy (full
dose and full cycles). The chemotherapy adverse events of NAC and AC had
no significate difference (25.4% VS 27.3%) in the NATCH trial. Thus,
the base case probability may change in the future with more and more
comparative research conducted about the NAC and AC.
Currently, there are no comparative studies that examine of quality of
life in NAC and AC for NSCLC patients. Hence, we assumed that the health
utility weight of NAC and AC is the same in various treatment stages. We
also used health utility weights from previously published literature
with NSCLC treatment phase related utilities used whenever possible.
There is also a difference in psychological effects after neoadjuvant
chemotherapy and primary surgery.