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.