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\begin{document}

\title{Cost-effectiveness of neoadjuvant versus adjuvant chemotherapy for
cT2-4N0-1 non-small cell lung cancer patients during initial treatment
phase}



\author[1]{Dongdong Wu}%
\author[1]{Juan Li}%
\author[1]{Yubo Wang}%
\author[1]{Hao Huang}%
\author[1]{Chunji Huang}%
\affil[1]{Third Military Medical University}%


\vspace{-1em}



  
  \date{May 15, 2024}


\begingroup
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\selectlanguage{english}
\begin{abstract}
Objective: The choice between neoadjuvant chemotherapy (NAC) and
adjuvant chemotherapy (AC) remains controversial in the treatment of
non-small cell lung cancer (NSCLC). There is no significant difference
in NAC and AC's effectiveness. We investigate the cost-effectiveness of
NAC versus AC for NSCLC. Method: A decision tree model was designed from
a payer perspective to compare NAC and AC treatments for NSCLC.
Parameters included overall survival (OS), surgical complications,
chemotherapy adverse event (AE), treatment initiation probability,
treatment time frame, treatment cost, and quality of life (QOL).
Sensitivity analyses were performed to characterize model uncertainty in
the base cases. Result: With the same overall survival, AC treatment
strategy produces a cost saving of Y=618.90 and an incremental
quality-adjusted life-years (QALY) of 0.10 years per patient. If median
OS of NAC is 1.4 months more than AC, NAC would be cost effective (CE)
at a Y=35446/QALY threshold. The model was robust enough to handle
variations to all input parameters except the overall survival. In the
probability sensitivity analysis, AC remained dominant in 54.6\% of
simulations. Conclusion: The modeled cost effectiveness analysis
indicates that with operable NSCLC, AC treatment is more cost effective
compared to NAC. If NAC provides a longer survival advantage, this
treatment strategy may be cost-effective. The OS is the main factor that
influences cost-effectiveness analysis.%
\end{abstract}%



\sloppy


Cost-effectiveness of neoadjuvant versus adjuvant chemotherapy for
cT2-4N0-1 non-small cell lung cancer patients during initial treatment
phase

\textbf{Abstract}

\textbf{Objective:}The choice between neoadjuvant chemotherapy (NAC) and
adjuvant chemotherapy (AC) remains controversial in the treatment of
non-small cell lung cancer (NSCLC). There is no significant difference
in NAC and AC's effectiveness. We investigate the cost-effectiveness of
NAC versus AC for NSCLC.

\textbf{Method:} A decision tree model was designed from a payer
perspective to compare NAC and AC treatments for NSCLC. Parameters
included overall survival (OS), surgical complications, chemotherapy
adverse event (AE), treatment initiation probability, treatment time
frame, treatment cost, and quality of life (QOL). Sensitivity analyses
were performed to characterize model uncertainty in the base cases.

\textbf{Result:} With the same overall survival, AC treatment strategy
produces a cost saving of Y=618.90 and an incremental quality-adjusted
life-years (QALY) of 0.10 years per patient. If median OS of NAC is 1.4
months more than AC, NAC would be cost effective (CE) at a Y=35446/QALY
threshold. The model was robust enough to handle variations to all input
parameters except the overall survival. In the probability sensitivity
analysis, AC remained dominant in 54.6\% of simulations.

\textbf{Conclusion:} The modeled cost effectiveness analysis indicates
that with operable NSCLC, AC treatment is more cost effective compared
to NAC. If NAC provides a longer survival advantage, this treatment
strategy may be cost-effective. The OS is the main factor that
influences cost-effectiveness analysis.

\textbf{Key words:} cost-effectiveness, neoadjuvant chemotherapy,
adjuvant chemotherapy, non-small cell lung cancer

\textbf{1. Introduction}

Non-small cell lung cancer (NSCLC) is a frequent malignancy and the most
common cause of cancer-related death among males and females in the
world, resulting in a large social and economic burden {[}1{]}. The
National Comprehensive Cancer Network (NCCN) guidelines recommended
surgery followed by adjuvant chemotherapy (AC) for cT2-4N0-1 NSCLC with
a supplementary instruction that neoadjuvant chemotherapy (NAC) followed
by surgery should also be considered for these patients {[}2{]}. The use
of adjuvant chemotherapy for surgically resectable NSCLC has been well
established by several randomized trials and meta-analysis which
demonstrated clear survival benefits over surgery alone {[}3{]}-{[}6{]}.

Despite similar overall survival (OS) and disease-free survival (DFS),
evidence is not very robust with regard to the benefits of NAC. The
original purposes of administering chemotherapy before surgery included:
improving operability by reducing tumor tissue size, increasing the
likelihood of administering the maximal planned dose of chemotherapy,
and reducing the likelihood of micro-metastasis and uncomplete resection
(R1/R2) {[}7{]}{[}8{]}. At the same time, there were several potential
risks of NAC including delaying operation, increasing the postoperative
complication for the postoperative toxicity, and making the tumor
unresectable {[}9{]}. Thus, there is widespread debate in the use of NAC
and adjuvant chemotherapy (AC) with NSCLC patients.

The comparative effectiveness of NAC versus AC in terms of DFS and OS
remains controversial. The study by Brandt et al. aims to evaluate
whether treatment strategy of NAC or AC is better for cT2-4N0-1 NSCLC
patients through a propensity score match analysis {[}10{]}. They
analyzed 92 matched-pair patients and ultimately demonstrated that there
was no significant difference in DFS and OS between treatment cohorts.
Previous investigators have tried to answer this question. The NATCH
trial recruited 624 patients with stage I-IIIA, N0-N1 NSCLC to compare the
effect of tree therapeutic strategies (NAC, AC and surgery alone)
{[}3{]}. The three arms trial found that there was no significant
difference in DFS and OS between those treatments. The open label
randomized trial by Westeel et al. showed the same conclusion in early
stage NSCLC patients {[}11{]}. Coincidentally, the meta-analysis of
trials also did not demonstrate differences in OS and DFS between NAC
and AC {[}12{]}{[}13{]}.

Although they show similar clinical outcomes, most of the previous
literature supports the use of adjuvant chemotherapy over neoadjuvant
chemotherapy. However, there are some theoretical difference that have
not been specifically addressed or adequately studied. Firstly,
administration of NAC could have the potential to reduce tumor size
before surgery and increase the complete resection rate {[}9{]}.
Secondly, the patients who received NAC might have higher surgery
complications and mortality rate, due to the preoperative chemotherapy
toxicity {[}7{]}. Thirdly, patients who received NAC had better
chemotherapy tolerance than the patients who underwent AC
{[}3{]}{[}10{]}.

These theoretical differences of AC and NAC may impact the cost
associated with caring for NSCLC patients. What's more, under the
circumstances, there was no robust evidence on the outcomes of NAC
versus AC in terms of OS and DFS. Cost-effectiveness analyses may
contribute to decision-making among NSCLC patients for whom the optimal
therapeutic regimen is unclear. Until now, cost-effectiveness research
comparing the NAC and AC treatment protocols in lung cancer, was absent.
The previous cost-effectiveness studies about neoadjuvant chemotherapy
versus adjuvant chemotherapy, focused only on ovarian cancer and head
neck cancer {[}14{]}-{[}19{]}. Therefore, we compared the cost
effectiveness of AC and NAC treatment strategies in the case of NSCLC,
through a decision tree-modeled cost effectiveness analysis from the
perspective of the payer.

\textbf{2. Method}

\textbf{2.1 Model Structure}

We created a decision tree model using the software package Tree-Age pro
2011 to compare the health and economic impact of NAC and AC for
cT2-4N0-1 NSCLC patients from the payer's perspective. Costs were
measured using a China Medicare care perspective, and outcome of
patients include OS, quality-adjusted life-years (QALYs), health
utilities value and treatment time. The utility of disease was
calculated according to published utilities. The event outcome was
informed by the study of Brandt et al.

In the model, patients received either surgery followed by four rounds
of adjuvant chemotherapy, or 2 rounds of neoadjuvant chemotherapy
followed by lung surgery and an additional 2 rounds of adjuvant
chemotherapy. We assumed that the chemotherapy regimen was intravenous
paclitaxel/carboplatin, for the previous study had showed there was no
significant association of chemotherapy regimens for NAC and AC and
there was no significant difference in the survival for different
chemotherapy regimens {[}10{]}. For every set of chemotherapy cycles,
patients could experience grade 3 or 4 chemotherapy-related adverse
events (AE). Meanwhile, during the surgery (both NAC and AC), the
patient also could experience surgery complications. If patients
experienced adverse event (AE) or complications from chemotherapy or
surgery respectively, they could either recover or die from the event
(Fig.1). The goals of our study were to compare the cost-effectiveness
in the initial stage and the treatment stage. Cost and prognosis for
patients treated with NAC and AC during the progressive stage were not
included in the model.

\textbf{2.2 Effectiveness and quality of life}

Based on the study by Brandt et al. there was no significant difference
in median OS between NAC and AC. So, we assumed that the OS (9.1 years)
and quality of life (QOL) during the progression-free survival stage
were equal. The parameters like probabilities of AE and complications,
are also based on the published results in this study, which are listed
in Table 1. As data for postoperative death couldn't be sourced, 30-day
mortality and 90-day mortality information was integrated into
postoperative complication mortality. Based on this study, the patients
treated with neoadjuvant chemotherapy all received the lung cancer
surgery, and the patients treated with surgery as the initial therapy,
all received the adjuvant chemotherapy. In considering chemotherapy
tolerance, we take the proportion of grade 3 and 4 AE as the input
parameter. There were no deaths for chemotherapy and AE.

Quality of life was estimated using standard health utility weights. The
utility weights of stable disease and progressive-free disease were
calculated according to published study{[}22{]}{[}24{]}. As there is a
shortage of quality of life studies in the case of lung cancer surgery
complications, we use the utility value of pneumothorax in the study of
Handorf et al {[}24{]} as the 3 and 4 grade complication utility
weights. The average utility weight of every treatment procedure was
shown in 2, and the time frames of each treatment are based on the study
of Dendulur et al {[}21{]} and Lugg et al {[}25{]}. In the model, the
treatment of patients with 3 or 4 grade AE will add 0.73 months to basic
time of surgery and chemotherapy, while treatment of those with 3 or 4
grade complication will add 0.5 months. The utility weight of patients
with 3 or 4 grade AE and complication were 0.45. The utility weight
during the time 6 months after initial treatment remained unchanged.

\textbf{2.3 Cost}

Because the study is about the treatment strategies in the initial
treatment phase, costs associated with the treatment of recurrence and
rehabilitation were assumed to be equivalent for NAC and AC. We also
assumed that the two treatment strategies have the same costs in
biochemical testing, imaging examination, pathological examination,
venipuncture related protocols and diagnosis. Cost calculation was only
done for medical related items that might make a big difference in NAC
and AC treatments.

Costs associated with (medical) caring for NSCLC patients included the
cost of surgery procedure, major late complications, chemotherapy
administration and chemotherapy adverse events (Table 3). The final cost
of the surgery was calculated as the sum of base surgical procedures
cost, additional surgical procedures cost and hospitalization costs
(including the cost of surgery complication). The surgery
(pneumonectomy, bi-lobectomy, lobectomy, or segmentectomy) costs were
constructed from Medical programs fee schedule (MPFS) {[}29{]}. The
chemotherapy costs were measured the costs of main chemotherapy drugs
(paclitaxel and carboplatin). Based on recommendations in the current
guidelines, one cycle chemotherapy dosing was calculated as 200
mg/m\textsuperscript{2} for paclitaxel and an area under the curve of 6
for carboplatin, {[}2{]}.

The major high-grade chemotherapy adverse events were neutropenia,
thrombocytopenia, anemia, nausea \& vomiting, febrile neutropenia,
diarrhea, hyperglycemia, arthralgias, myalgias, fatigue, sensory
neuropathy, allergic reaction {[}3{]}. The definition of AE grade
followed that of the Common Terminology Criteria for Adverse Events
(CTCAE) Version 5.0 {[}27{]} and Guideline of Chinese Society Oncology
(CSCO) Primary Lung Cancer {[}28{]}. The costs of AE were estimated by
the core medical care components, empirical data, and expert opinion on
implementation strategies.

All cost estimates were calculated in 2020 RMB with the medical care
components of Consumer Price Index from the price dataset in Shanghai
Price Control Administration {[}29{]}.

\textbf{2.4 Sensitivity analyses}

To address the uncertainty of costs and outcomes in our model, we did
several 1-way sensitivity analyses. The parameters listed in Table 1-3
were varied in specified ranges of NAC and AC. In the base case
analysis, a chance of \selectlanguage{ngerman}±20\% to the original values were set as the upper
and lower bounds.

We also performed probabilistic sensitivity analysis (PSA) to assess the
combined effect of multiple parameter uncertainty on the increment cost
effectiveness ratio (ICER.) Factors varied in sensitivity analyses
included OS, base-case event probabilities, costs, utility weights and
treatment time frames with distributions selected based on published
literature {[}30{]}. Costs and treatment time obeyed gamma distribution,
with a standard error of 10\% of the original value. Utilities
distribution reflected a triangular distribution, and base-case utility
weights were set as the most likely value with minimum and maximum
values of ±0.05. Events probabilities distribution were beta
distribution, with alpha and beta parameters calculated through the
event's incidence in the study of Brandt et al {[}10{]}. The PSA was
performed with 10,000 Monte Carlo simulations across all distributions.

\textbf{3. Result}

\textbf{3.1 Base case}

In this model AC is the dominant strategy with the ICRE of 6384.14
RMB/QALY. Using the base case probabilities and cost, AC cost is
105,453.73 RMB per patient which is 618.90 RMB fewer than NAC cost (with
106,072.63 per patients) in the initial treatment phase. And the average
QALY is 8.66 years for AC, and 8.56 for NAC.

\textbf{3.2 Sensitivity Analyses}

We performed a series of one-way sensitivity analyses to discover the
effect of the different parameter's uncertainty on the ICER. The Tornado
diagram shows the effect of those parameters that led to a change in the
ICER from big to small (Fig.2). In our model, the ICER was most
sensitive to variations in OS of NAC and AC and, when the medial OS of
NAC is 0.12 years more than AC, the NAC will be cost-effective at the
35,446 RMB threshold (Fig.3). The sensitivity analyses suggest that our
model was robust enough to handle to the uncertainty surrounding the
parameters of cost, base case probabilities, treatment time and utility
weight.

In PSA, AC was the dominant strategy in 64.3\% of 10,000 Monte Carlo
simulations and, at the threshold of 35,446 RMB per QALY, there is a
54.1\% probability that AC strategy is cost-effective. Fig.4 shows the
corresponding cost effectiveness acceptability curve for NAC and AC.
Therefore, the statement about basic simulation regarding incremental
costs, effectiveness and the ICER, is stable.

\textbf{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 \selectlanguage{english}Y=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 \selectlanguage{english}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 \selectlanguage{english}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 \selectlanguage{english}I disease patients who did not benefit from
chemotherapy in the study cohort is 75\%. In~comparison~with the
meta-analysis {[}12{]}, the stage \selectlanguage{english}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
(\textless{}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.

\textbf{5. Conclusion}

Despite the higher levels of chemotherapy tolerance and the same
survival rate in NAC, AC has a favorable cost-effectiveness profile in
the NSCLC initial treatment phase. The cost-effectiveness analysis is
sensitive to survival at classic WTP thresholds. Based on the current
evidence, the NAC as a general ffirst choice treatment in cT2-4N0-1
NSCLC patients is not supported. To better assess the relative merits of
these therapeutic regimens, attention should not only be given to OS and
DFS, but also to the QOL and cost-effectiveness, especially when the
number of lung cancer patients and treatment burden increase greatly. In
view of the insufficiency in head to head trials and clinically enriched
dataset, the cost-effectiveness analysis of NAC and AC can benefit from
more cohort studies.

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