Patients and treatment options
Breast cancer patients were registered in the Breast Cancer Information Management System of West China Hospital, Sichuan University (Sichuan, China) since 1989. Their medical history, pathological diagnosis, and treatment information were prospectively collected by oncologists. Each patient was followed by outpatient visit or telephone at 3 to 4-month intervals within 2 years after diagnosis, 6-month intervals within 3~5 years, and then annually. Written informed consent was provided by all the patients. Ethical permission was granted by the Ethics Committee, West China School of Medicine/West China Hospital, Sichuan University(approval number 2017-255).
Because the baselines of the three groups were not consistent, we used R software to match the propensity scores. Based on the MAST + RECON group, the nearest-neighbor method was used for 1:1 matching. The rest of the statistics were performed using SPSS 25.0 software. The measurement data were analyzed by analysis of variance, the unordered counting data were tested by row × list chi-square tests, and the ordered counting data were tested by rank sum. All the tests were two-sided, and p <0.05 indicated statistical significance.
The Markov model of early breast cancer identified in this study has four states: disease-free survival, local recurrence, distant metastasis, and death. The model was based on the following hypothesis: patients with disease-free survival can develop local recurrence and distant metastasis, patients with local recurrence can develop distant metastasis, and only patients with distant metastasis may have breast cancer-related death. It was assumed that all patients were at risk of death from causes other than breast cancer. Once a patient dies, they cannot transition to other states, so death was also an absorbed state (Fig. 1). It is assumed that the survival rate of patients (such as PFS and OS) can be extrapolated to the Markov model.
There are three alternative surgical options for confirmed early breast cancer patients: MAST+ RECON, BCT, and MAST. The initial age of the cohort after the propensity score in this study was 39 years. Therefore, the Markov model simulates the 60-year outcome of patients after receiving the three surgical routes. The status of all the patients entering the model was the disease-free survival status.
The utility analysis used quality-adjusted life years (QALYs), and then weighed the advantages and disadvantages of the three surgical treatment approaches. The main outcome measure used in the model was the ICER, which was the incremental cost-effectiveness ratio (ICER), that is, the ratio of the difference between the relative costs and effects of the intervention plan and those of the control plan. When comparing the ICER with the threshold, if the ICER is less than the threshold, it means that the solution is cost-effective; if the ICER is greater than the threshold, the solution is not cost-effective. The threshold for this study is WTP, which uses 3 times China's per capita GDP in 2018[19,20], or US $27,931.04.
TreeAge Pro 2011 (TreeAge Software, Inc., Williamstown, MA, USA) was used to build and analyze the Markov model. This software is professional software for decision trees and Markov models. This study used a 3% discount rate to discount costs and utility values and applied a half-cycle correction.
In this study, the transition probability was determined by survival analysis to obtain the time to transition from one state to another state, and then the transition probability was calculated by the formula. According to the calculation formula of transition probability, i.e.,r=-[ln(1-P1)]/t1 and P=1-, the transition probability was calculated. For example, the follow-up time from modified disease-free survival to local recurrence in this study was 94 months, with a cumulative recurrence-free probability P1 of 0.994. A Markov cycle was 12 months in a year, and the unit of follow-up time was converted from month to year to obtain the parameter t1. Because the annual local recurrence probability was calculated, t2 = 1 was taken. The local recurrence probability of MAST was calculated by the formula as 0.000768.
This research considered the direct and indirect costs from the perspective of the whole society. All costs were expressed in US dollars ($), and the exchange rate was US $1= 6.93 yuan (January 13, 2020). The direct cost was calculated as the direct medical costs and the patient's transportation expenses, and the indirect cost included the patient's lost time. Direct medical costs were derived from all inpatient and outpatient records of patients in the electronic medical record system and were collected according to the state Markov model. Since these costs came from the electronic medical record system, which included all treatment and expense records of the patient, out-of-pocket expenses were also included. the patient's expenses include hospitalization and outpatient expenses in the following periods: the first year of treatment, the first year of recurrence, distant metastases each year, and the three months before death. Since the patient has no hospitalization expenses during the follow-up process, the follow-up expenses consist of outpatient expenses. The patient’s hospitalization costs included diagnosis, treatment, surgery, anesthesia, drugs, radiotherapy, materials, monitoring, etc. The costs for outpatients included appointments, examinations and medicine, etc. The use of resources after recurrence involved surgery, radiotherapy, chemotherapy, hormone therapy, etc., including inpatient and outpatient records.
This study also considered the first year of transportation costs for patients in different surgical treatment groups. The calculation of transportation costs was considered as the sum of the number of inpatient and outpatient visits × the average transportation cost per visit. The average transportation cost of each visit referred to the related literature published by Chengdu, China, on health economics evaluation. Based on taxi fares, the transportation cost was set at 80 yuan/time.
The calculation of the cost of lost work in this study was based on the sum of the average number of days of hospitalization and the number of outpatient visits in the first year of treatment for patients in different surgical treatment groups × average daily lost time. By calculation, the loss time in the MAST + RECON group was 47 days, the loss time in the BCT group was 39 days, and the loss time in the MAST group was 44 days. According to the announcement issued by the Statistics Bureau of Sichuan Province of China, the average daily wage of employees in all units of Sichuan Province in 2018 was $9338.67/year, calculated as $25.59/day. Therefore, the lost labor cost of the MAST+ RECON group was calculated to be $1202.54, the lost labor cost of the BCT group was $940.71, and the lost labor cost of the MAST group was $1125.78.
It was necessary to determine the health utility value of the patients of the three surgical treatment plans within one year of treatment, after the second year or more, the cases of relapsed breast cancer within one year (state R) and those of metastatic cancer (state M). The EQ-5D-5L scale was used to investigate the health utility value of 446 Chinese breast cancer patients. The health utility value of recurrent breast cancer within one year (state R) was 0.779, and the health utility value of metastatic cancer (state M) was 0.737. The health utility values of patients undergoing BCT and MAST were also obtained from the survey. Since only 3 of the 446 patients surveyed underwent MAST + RECON, the health utilities of this surgical treatment group could not be calculated. Therefore, we used the health utility mapping model established earlier in this research group to map the value of FACT-B to EQ-5D-5L to obtain the health utility of this type of patient. The value of FACT-B in breast cancer patients undergoing breast reconstruction surgery was taken from the literature[24,25], and we calculated the average value of FACT-B reported in these studies.
A one-way sensitivity analysis was performed to test the robustness of the economic model and the impact of the key input parameters on the results. The results of one-way sensitivity analysis were represented by tornado diagram. The upper and lower limits of 95% CI were used as the upper and lower limits of the parameter change, and the remaining parameters adopted ± 20% of the baseline value as the upper and lower limits for the parameter changes. The discount rate was set at 0% and 5% as the upper and lower limits, respectively.
For Probabilistic Sensitivity Analysis (PSA), 1,000 iterations of Monte Carlo Simulation was developed to evaluate the uncertainty strategy and the results were expressed as cost-benefit acceptability curves. The distribution function was assigned to each variable of PSA to evaluate the robustness of the result. As far as the allocation for PSA is concerned, for utilities and transition probabilities, use the beta distribution, and for costs, use the lognormal distribution. The result of probability sensitivity analysis was expressed as cost- effectiveness acceptability curves.