Economic Evaluation of Neoadjuvant Versus Adjuvant Chemotherapy in Cancer Treatment: A Systematic Review and Meta-analysis

doi:10.21203/rs.3.rs-1631613/v1

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Economic Evaluation of Neoadjuvant Versus Adjuvant Chemotherapy in Cancer Treatment: A Systematic Review and Meta-analysis

https://doi.org/10.21203/rs.3.rs-1631613/v1

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published 01 May, 2024

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Background: In the absence of evidence on whether neoadjuvant or adjuvant chemotherapy is more beneficial for various tumor treatments, economic evaluation can assist medical decision-making. There is limited evidence on their cost-effectiveness and their prospective evaluation is less likely in the future. Therefore, a systematic review and meta-analysis help compare these therapies from various perspectives.

Methods: Various databases were searched for studies published from inception to 2021. This study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses reporting guidelines and economic specific guidelines. The data were pooled using a random effects model when possible.

Results: The retrieval identified 15 economic evaluation studies of neoadjuvant and adjuvant chemotherapy in eight types of cancer. Neoadjuvant chemotherapy is the dominant strategy for pancreatic, head and neck, rectal, prostate, and cervical cancers, and colorectal liver metastases. For ovarian cancer, neoadjuvant chemotherapy is cost-effective with a lower cost and higher or similar quality-adjusted life year. There were no significant differences between the therapies in cost and outcomes for lung cancer. For stage IV or high-risk patients with ovarian or prostate cancer, neoadjuvant chemotherapy was cost-effective but not for patients who were not high risk.

Conclusion: The economic evaluation results of the included studies were inconsistent because of their different model structures, assumptions, cost inclusions, and a shortage of studies. The proportion of the high-risk study population, such as stage IV, age ≥ 70 years, and comorbidity was consistent in some types of cancer.

Economic evaluation

Neoadjuvant chemotherapy

Surgery

Adjuvant chemotherapy

Cancer treatment

Surgery is the preferred treatment option for many cancers. However, surgery alone is insufficient for cure or a complete resection in all patients. Many clinical trials and meta-analyses indicate that preoperative or postoperative chemotherapy can improve survival. The National Comprehensive Cancer Network (NCCN) guidelines recommend adjuvant chemotherapy (AC) in many middle- and advanced-stage cancers as standard treatment after resection.¹ Furthermore, the NCCN recommends neoadjuvant chemotherapy (NAC), in which chemotherapy is administered before and after surgery.

The recommendation for AC and NAC is based on multiple phase III randomized controlled trials (RCTs) and a meta-analysis.^2–12 These studies demonstrated NAC and AC benefits for survival, over no chemotherapy. However, survival rates related to NAC and AC do not differ significantly in various cancers. The outcomes of NAC and AC include complications, postoperative mortality and resection rates and do not affect overall survival (OS) or progression-free survival (PFS).

The choice of NAC and AC remains controversial in cancer treatments.^2–14 NAC can reduce tumor size and increase operability. Moreover, AC promotes timely resection of the tumor and fewer surgical complications.^13,14

In the absence of evidence on which treatment strategy is more beneficial, economic evaluation (EE) can edify medical decision-making. EEs have been conducted on various cancers. They highlighted the importance of comparing economic outcomes of NAC and AC in disease treatment and policy formulation. This paper aims to systematically identify literature on the EE of NAC and AC, analyze the economics and outcomes of NAC vs. AC in specific cancers, and review the quality of these studies, including their strengths and limitations. To the best of our knowledge, this is the first systematic review to provide an EE of NAC and AC in cancer patients.

2.1 Search Strategy

The research strategy included performing a literature search for EEs of NAC and AC treatment for cancer patients from inception to September 8, 2021, in the Embase, PubMed, and Centre for Reviews and Dissemination (CRD) databases. Search strategies are included in Table S 1 in the Supplement.

2.2 Study Selection

Two authors reviewed and analyzed identified articles to ensure adherence to the following inclusion criteria.

Articles are written in English.
Articles compare NAC with AC intervention strategy in EE (only cost or effectiveness analyses were excluded) for cancer patients.
Articles contain the full text.
Articles or studies describe the research result.

2.3 Risk of Bias and Quality Assessment for EE

Cochrane risk of bias in nonrandomized studies of interventions for non-RCT studies was used.¹⁵ Additionally, Drummond’s 10-item checklist was employed to assess the methodological rigor of the EE of each article.¹⁶ It is an objective scoring system that measures the transferability of an EE. This robust checklist allows comparisons between different EEs, and is recommended by internationally recognized guidelines.¹⁷ We ascertained that studies followed the criteria in Risk of Bias in Nonrandomized Studies of Interventions (ROBINS-I) and Drummond.^15,16

2.4 Data Extraction and Statistical Analysis

The data on the author, year, country, data source, results of EEs, and the authors’ conclusions were extracted. Other extracted data included the study design, time horizon, evaluation method and model, sensitivity analyses, and perspective.

Data were collated using Microsoft Excel (version 2016) and analyzed using RevMan (version 5.3). Costs were converted to US dollars with an annual average historical parity rate and adjusted for inflation to 2021, discounted at an annual rate of 3%.¹⁸ The year for the commencement of the inflation adjustment was assumed to be one year before the article’s publication, unless specified in the article. Effects such as survival time and quality-adjusted lifetime were converted to years. Where possible, differences in costs and effects were pooled in the meta-analysis. The PRISMA guidelines provided flexibility to the framework to incorporate economic-specific guidelines for quality assessments and results’ analyses.¹⁹ The willingness-to-pay (WTP) threshold was $50,000/ quality-adjusted life year (QALY), unless otherwise declared.²⁰

3.1 Search Results

The literature search resulted in 1256 records. After screening citations by title and abstract, 41 papers were included in a full text review. Subsequently, 15 papers met the inclusion criteria (Fig. 1).

[Insert Fig. 1 about here]

3.2 Quality Assessment Results

The Cochrane ROBINS-I tool indicated that seven studies had a low risk of bias (ROB), eight had moderate ROB, and none had a high ROB (Figures S 1 and S 2 in the Supplement). Four studies had moderate ROB due to hypothetical cohorts with different outcome resources, three because of the exclusion of individuals with missing information, and one due to N2 nodal disease probability difference (100% vs. 0) in the NAC and AC groups. The Drummond checklist assessment indicated that all studies were of high quality (satisfying ≥ 7 items). Seven studies dropped points for cost adjustment for differential timing, six for incremental cost and consequence analysis, and four for uncertainty analysis (Figures S 3 and S 4 in the Supplement).

3.3 Characteristics of Included Studies

The included studies performed the health economic estimation of NAC and AC in eight regions: cervical, head and neck, lung, ovarian, pancreatic, prostate, and rectal cancers, and colorectal liver metastases (Table 1). Most studies were published after 2011. Of the research, 67% was conducted in the US, and global representations included studies on China, Italy, Germany, Romania, and Japan.

Table 1

Characteristics of included studies
Reference	Country	Disease	Cohort Type	Analysis type	Analysis method	Time horizon	Perspective	Discount	Currency and year reference	Cancer staging	Data resource
Rocconi 2005²¹	USA	cervical cancer	HC	CE	DTM		third-party payer		2003 US	IB2	published literature
Ercolani 2011²²	Italy	colorectal liver metastases	HC	CE	MM	10 Y	payer (social)	0.03	2010 EUR	-	published literature
Himmel 2013²³	Germany	head and neck cancer	HC	CE	MM	5 Y	health insurance	0	2009 EUR	locally advanced	EORTC 22931 CRT
Ferguson 2003²⁴	USA	lung cancer	HC	CE	DTM	-	medical center	-	2002 US	>Ⅲ	published literature
Wu 2021²⁵	China	lung cancer	HC	CE	DTM	1 Y	provider	0.03	2020 CNY	cT2-4N0-1	published literature
Tran 2018²⁶	USA	ovarian cancer	HC	CE	MM	7 M	health care system	-	2015 US	advanced	M-CRT
Onisim 2019²⁷	Romania	ovarian cancer	RC	CE	MB	-	-	-	2008 US	IIIC-IV	NWRCR
Poonawalla 2015²⁸	USA	ovarian cancer	RC	CE	PS and PLD	-	payer	0.03	2009 US	III-IV	SEER
Cole 2018²⁹	USA	ovarian cancer	HC	CE	DTM	1 Y	Medicare payer	-	2015 US	IIIC-IV	SCORPION CRT
Forde 2016³⁰	USA	ovarian cancer	RC	CE	MM	7 M	-	0.03	2010 US	IIIC-IV	SEER
Rowland 2015³¹	USA	ovarian cancer	HC	CU	MM	5 Y	health care system	0.03	2010 US	III-IV	EORTC 55971 CRT
Abbott 2013³²	USA	pancreatic cancer	RC	CE	DTM	-	payer	0.03	2011 US	Ⅰ-Ⅳ	ACSNC/NSQIP
Choi 2019³³	USA	pancreatic cancer	HC	CE	MM	-	third-party payer	0.03	2018 US	BRLD	published literature
Matsumoto 2017³⁴	Japan	prostate cancer	RC	CE	-	5 Y	-		US	T2c or T3	MUCSG
Caturegli 2020³⁵	USA	rectal cancer	RC	CE	-	-	provider	-	US	Ⅱ-Ⅲ	ARIA database
Abbreviation: NS not specified, CE cost-effectiveness, CU cost-utility, OCC outcome and cost comparison, CC cost comparison, MM Markov model, DTM decision tree model, PS propensity scores approach, PLD person-level data approach, MB median-based approach, BRLD borderline resectable or locally advanced, RCT randomized controlled trials, SEER Surveillance, Epidemiology, and End Results Program database, NWRCR North-Western Regional Cancer Registry database, ACSNC/NSQIP American College of Surgeons National Cancer database /National Surgical Quality Improvement Program, MUCSG Michinoku Urological Cancer Study Group database, HC hypothetical cohort, RC retrospective cohort, Y year, M month

[Insert Table 1 about here]

The study population in nine studies had ≥ stage III cancer. Three included populations at all stages, and one included stage I only. The mean age of the study population was > 56. Three studies did not report the age. Payer’s perspectives (including health insurance and third-party payers) were the most common (n = 7). Five varied across healthcare systems and providers. One claimed to have a social perspective, but did not document indirect medical or social costs and was classified as a payer perspective.²²

The analysis type of most EEs was cost-effectiveness (n = 14). One study performed a cost-utility analysis. The study method included the EE model approaches, such as the Markov model (n = 6) and decision tree model (n = 5), and an approach to estimate the incremental cost-effectiveness ratio (ICER) and net monetary benefit (NMB) value directly,^26,28 such as the median-based method with censored data,³⁶ net benefit regression, and propensity score approach.^37,38

Although time horizons were not always clearly specified or justified (n = 12), eight studies described them to be ranging from 7 months to 10 years. Seven specified the cost discount rate and applied it to 3%; most estimated the cost in US $ (n = 11), while the remaining studies used the EURO (n = 3) and CNY (n = 1). The year of currency reference ranged from 2002 to 2020.

The cost categories in the EEs varied across the studies (Figure S 5 in the Supplement). Fourteen studies estimated the cost using the phase-of-care method, one with the total cost estimation method with all cost categories. More than two-thirds included surgery, chemotherapy/chemoradiotherapy, and hospitalization costs. Half of the studies estimated the cost of surgery complications and chemotherapy-related adverse event (AE) treatment costs. One assessed the cost of granulocyte-colony stimulating factor; classified as the cost of AE.³⁴

3.4 Study Results

The original costs, effectiveness, and EE results of the included studies are presented in Table 2. The costs were adjusted to 2021 US monetary rates. The study population is indicated in Table S 2 in the Supplement. The results of the NAC and AC comparisons were mixed (Fig. 2). Three studies with no definitive conclusion, reported conflicting results across treatment arms or from sensitivity analyses, indicating the dependence of cost-effectiveness on parameter specification in the model (Table S 3 in the Supplement). Six conducted probabilistic sensitivity analyses (PSA), in which all input parameters were within specified bounds of uncertainty. Eight conducted a series of one-way sensitivity analyses to determine the impact of the altered variable on the modeled outcome. Most analyses showed that OS and PFS were the main determinants of the cost-effectiveness of the two strategies. Three studies indicated that the possibility of a dominant strategy is less than 60%, which implied that the result of cost-effectiveness is not sufficiently stable. One showed that the result was stable under $100,000 and $150,000 WTP, but not under $50,000.

Table 2

Results of EE
Reference	Subgroup	WTP	Cost ($)		Effect (QALY)		ΔCost	ΔEFF	Dominant strategy	ICER (QALY)
Reference	Subgroup	WTP	NAC	AC	NAC	AC	ΔCost	ΔEFF	Dominant strategy	ICER (QALY)
Rocconi 2005²¹		NS	29900.00	28400.00	0.693(5-y)	0.69 (5-Y)	-	-	NAC	16659.4/survivor
Ercolani 2011²²		3,000€	27054.00€	25874.00€	39.33	37.84	1180.00	1.490	NAC	792
Himmel 2013²³		38,000€	17377.00€	13999.00€			3378.00	0.100	NAC	33780
Ferguson 2003²⁴		$50,000	27000.00	19000.00	1.80	1.30	8000.00	0.500	NAC	16000
Wu 2021²⁵		¥35,446	¥112064.63	¥108999.73	8.56	8.66	3064.90	-0.100	AC	Cost saving
Tran 2018²⁶		$50000-$100,000	20762.00	27796.00	0.38	0.35	-7034.00	0.035	NAC	Dominant
Onisim 2019²⁷		$50,000	3580.41	2990.19	3.34	3.57	590.22	-0.230	AC	Cost saving
Poonawalla 2015²⁸	all	$50,000	134576.30	117159.00	3.10	3.00	17417.30	0.100	AC	Cost saving
Poonawalla 2015²⁸	Non-HR	$50,000	126230.00	108493.80	3.00	3.30	17736.20	-0.300	AC	Dominant
Poonawalla 2015²⁸	HR	$50,000	116213.10	80681.80	2.90	2.10	35531.30	0.800	NAC	44414
Cole 2018²⁹		$50000-$100,000	17000.00	26450.00	0.720	0.606	-9450.00	0.114	NAC	Dominant
Forde 2016³⁰	IIIC	$50,000	30016.00	31945.00	1.69	1.79	-1929.00	-0.100	AC	19290
Forde 2016³⁰	IV	$50,000	27338.00	31869.00	1.66	1.69	-4531.00	-0.030	NAC	151033
Rowland 2015³¹		$100,000	25849.00	31465.00	24.23	24.23	-5616.00	0.000	AC	Cost saving
Abbott 2013³²		NS	36583.00	46830.00	18.80	8.70	-10247.00	10.100	NAC	Dominant
Choi 2019³³	GEM/CAPE	$100,000	174250.00	160400.00	2.91	2.61	13850.00	0.300	NAC	46167
Choi 2019³³	GEM	$100,000	174250.00	130300.00	2.91	2.25	43950.00	0.660	NAC	66591
Matsumoto 2017³⁴		NS	5634.2	9833.3	0.86 (5-Y)	0.59 (5-Y)	-4199.10	0.270	NAC	Dominant
Caturegli 2020³⁵		NS	33727*	33782*	0.7 (5-Y)	0.7 (5-Y)	-55.00	0.000	NAC	Cost saving
* The data is from figure through Engauge Digitizer 11.1 Abbreviation: EE economic evaluation, HR high risk, ΔCost Increment cost, ΔEFF increment effect, 5-Y 5 years survival rate, AC Adjuvant chemotherapy, ICER incremental cost-effectiveness ratio, NAC neoadjuvant chemotherapy, WTA willingness-to-pay

[Insert Table 2 about here]

3.4.1 Cervical Cancer

A hypothetical cohort study analyzed the cost-effectiveness of NAC and AC in 10,000 patients with clinical stage IB2 squamous cell carcinoma using a decision analysis model.²⁴ NAC and AC treatments resulted in similar 5-year disease free survival rates (69.3% and 69.0%, respectively), which were set as the effective rates and cost $299M and $284M per 10,000 patients, respectively. The study showed that AC was the dominant strategy, with an ICER of $16,659/survivor.

3.4.2 Colorectal Liver Metastases

Another study reported the EEs for colorectal liver metastases in Italy.²² Under the WTP of 3,000€, AC was the dominant strategy with an ICER of 792€/QALY. Its cost was estimated from a social perspective, but socially related costs were not included.

3.4.3 Head and Neck Cancer

Yet another study investigated the cost-effectiveness of NAC and AC in locally advanced operable head and neck cancer using a Markov model.²³ The costs for AC amounted to 13,999€. NAC raised the costs to 17,377€ with a higher effectiveness of 0.1 years of life. The NAC strategy is cost-effective when the WTP is set to 38,000€. The ICER with varied side effects for NAC and AC cost between 31,520€ and 36,050€ per life year.

3.4.4 Lung Cancer

Two studies used a decision tree model to analyze the cost-effectiveness of treating non-small cell lung cancer (NSCLC) during the initial treatment phase.^24,25 Meta-analyses were conducted for QALY and costs (Fig. 2), although one study used a hypothetical cohort, the base-case event probabilities and effectiveness came from one and the same study.³⁹ There was no significant difference in cost (standard mean difference: 1.90, 95% CI -1.28 to 5.07) and QALY (mean difference: 0.23; 95% CI -0.35 to 0.82).

[Insert Fig. 2 about here]

Two studies were conducted in different countries with different study populations. The N2 nodal disease patient probability in NAC (100% vs. 12%) and AC (0% vs. 15%) for the two studies were different, and the cost of treatment came from two different countries.

3.4.5 Ovarian Cancer

Six studies report the EEs of NAC and AC in ovarian cancer.^26–31 Moreover, one conducted a hypothetical cohort with the event probabilities and QALY from different studies,²⁶ which could not be included in the meta-analysis. The study showed that NAC was with lower cost and higher QALY.

A meta-analysis was conducted by pooling five studies.^27–31 The combined cost (standard mean difference: -0.94, 95% CI -1.82 to -0.06) and QALY (mean difference: -0.01, 95% CI -0.09 to 0.07) are in Fig. 2. Results revealed that NAC had a lower cost, and no significant difference in QALY. A sensitivity analysis illustrated that four studies conducted in the US indicated no significant difference in QALY (mean difference: 0.02, 95% CI-0.06 to 0.10).^28–31 The result indicated that NAC was cost-effective with similar effects and lower costs, like the hypothetical cohort study.²⁶

In six studies, two conducted subgroup analyses for high-risk patients (IV, age ≥ 75, comorbidity) and those who are not. A meta-analysis could not be performed for the study that did not present the high-risk and not high-risk study population. Two demonstrated that NAC was cost-effective in high-risk and stage IV patients.

3.4.6 Pancreatic Cancer

Two studies reported that NAC was cost-effective for pancreatic cancer.^32,33 Meta-analyses could not be performed because one used a hypothetical cohort with multiple source outcomes. While two studies had the same result, NAC was the cost-effective treatment strategy.

3.4.7 Prostate Cancer

One study retrospectively analyzed the cost-effectiveness of NAC and AC in high-risk patients with prostate cancer.³⁴ The cost of AC was $4,174 higher than that of NAC. The 5-year biochemical recurrence-free rates of AC and NAC were 59% and 86%, respectively. Although the cost and OS of NAC and AC were not reported explicitly, NAC dominated with lower cost and better outcome.

3.4.8 Rectal Cancer

One study performed a cost-effectiveness analysis for NAC and AC in patients with histologically proven stage II or III rectal adenocarcinoma.³⁵ The total cost of NAC was $55 less than that of AC, and the OS and PFS of the two strategies were not significantly different.

To the best of our knowledge, this is the first systematic review to implement an EE of NAC and AC in cancer. We found that the EEs of NAC or AC dominant approaches for multiple cancer patients, are mixed, although 68% of studies revealed NAC as the dominant strategy. NAC is more cost-effective for pancreatic, head and neck, rectal, prostate, and cervical cancers, and colorectal liver metastases. NAC is cost-effective with lower cost and similar QALY in ovarian cancer, and there were no significant differences in cost and outcomes for lung cancer.

For high-risk patients (IV, age ≥ 75 years, comorbidity) with ovarian cancer, NAC was cost-effective with higher QALY and similar cost, with the opposite result for patients who are not high risk.²⁸ In prostate cancer,³⁴ NAC was the dominant strategy in high-risk patients (clinical stage T2c or T3, initial prostate-specific antigen levels ≥ 20 ng/mL, biopsy Gleason score (GS) ≥ 8 according to the D’Amico risk stratification system).⁴⁰

Patient numbers at various stages (Table S 4 in the Supplement), revealed NAC as dominant among majority of patients at stage IV.

One study indicated NAC as prevalent with stage IV patients, while AC was dominant in stage III. One with a 65% stage IV patient proportion showed that NAC was cost-effective.

Thus, NAC is cost-effective in stage IV or high-risk patients because they have more complications or operational difficulties, which can offset AC’s surgery advantage.

Given the number and types of cancer research designs, it is difficult to synthesize and analyze all cancer types. Some studies use observational cohorts, with patient populations and data from the medical database. Only one RCT study and four population studies come from RCTs, probably because they are not cost effective in NAC and AC. In addition, the RCT for effective outcomes comparisons in NAC and AC lacked detailed cost information.

In addition, the literature conveys uncertainty regarding cost and efficacy. The efficacy results align with previously published research,^41–48 demonstrating no significant advantages of NAC and AC for survival. The variation in efficacy was mostly due to the proportion of patients with different stages of cancer, which relates closely to survival time.^49,50 Moreover, studies with different time horizons and consequences were measured in different units.

The cost variation can be attributed to several factors. The studies employed various designs and methods to measure and analyze cost differences, such as the phase-of-care and total cost estimation methods. Furthermore, with different assumptions, the cost categories included were different, as in Figure S 5 in the Supplement. Another driver could be the inclusion of studies from multiple countries and settings, as costs can vary country wise. We found that cost comparisons differ, even for the same cancer (Table 2). Some included studies assumed that AE costs were equal in NAC and AC, while Brandt et al. found that patients’ tolerance of NAC was better than that of AC in NSCLC.³⁹ Similar results were also found in ovarian,⁵¹ breast,⁵² and gastric cancer.⁵³ Therefore, we considered that chemotherapy AEs’ costs required inclusion in the model. Moreover, the quality of life (QOL) of patients experiencing NAC and AC must be different owing to different tolerances.⁵¹ We need to evaluate the utility value of NAC and AC, respectively, which are equivalent in the reviewed studies.

The comparison of the efficacy of NAC and AC in these eight types of cancer remained controversial, which showed that OS or PFS were not significantly different.^41–48 Two treatment strategies are recommended by the NCCN.¹ However, there are numerous systematic reviews about NAC and AC comparisons in multiple cancers. Burotto et al. showed that NAC and AC had no statistically significant difference in OS for breast or colorectal cancer.⁵⁴ Our review analyzes the cost-effectiveness of NAC vs. AC from a broad scope and spectrum of disease, making the study more informative, relevant, and useful for clinical practice and medical decision-making.

Although EEs’ results are uncertain, the PSAs suggested that NAC was likelier to be cost-effective at each WTP level. Most possibilities of the dominant strategy in PSA were less than 60%, and the survival, recurrence rate, R0 rate, and QOL can change the results of cost-effectiveness analysis. Therefore, a study of the effect of NAC vs. AC should consider these outcomes.

In our review, stage IV or other high-risk cancer patients were more suitable for NAC. Therefore, subgroup analyses, such as in Forde et al.^30,55 and Poonawalla et al.²⁸ on NAC and AC EEs or other studies are necessary. Furthermore, the more elaborate the high-risk assessment before treatment, the more benefit patients received in medical decisions.

This review is constrained by the quantity and quality of the included studies, which may lead to inaccurate analysis and synthesis. The quality of reporting was high for most studies, but some failed to clearly define and report on perspectives, cost inputs, or study design elements of the economic model. In addition, 83% of included studies were based on observational data, which may have resulted in biased parameter estimates. Furthermore, this study is limited by summarizing results from published studies, thus potentially missing important unpublished data.

There is moderate-quality evidence that NAC is cost-effective for stage IV or other high-risk patients in the initial treatment for ovarian cancer, and AC is cost-effective for patients who are not high-risk. For other cancers, including rectal, bladder, breast, cervical, head and neck, pancreatic, and prostate cancers, and colorectal liver metastases, there is limited evidence on whether NAC or AC is dominant. The difference in QOL and cost shows that EEs of NAC and AC are important to enhance value in policy formulation and medical decisions. Therefore, more high-quality EE studies on NAC vs AC in initial cancer treatment are necessary, particularly in subgroup and total cost-related analyses.

Funding: This study was funded by Chongqing Science, Technology and Health Joint Project (2022QNXM033).

Conflict of interest statement: The funding organizations had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication. The authors have no conflicts of interest to declare.

Acknowledgements: We would like to thank Editage (www.editage.cn) for English language editing.

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No competing interests reported.

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Economic Evaluation of Neoadjuvant Versus Adjuvant Chemotherapy in Cancer Treatment: A Systematic Review and Meta-analysis

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Abstract

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1. Introduction

2. Method

2.1 Search Strategy

2.2 Study Selection

2.3 Risk of Bias and Quality Assessment for EE

2.4 Data Extraction and Statistical Analysis

3. Results

3.1 Search Results

3.2 Quality Assessment Results

3.3 Characteristics of Included Studies

3.4 Study Results

3.4.1 Cervical Cancer

3.4.2 Colorectal Liver Metastases

3.4.3 Head and Neck Cancer

3.4.4 Lung Cancer

3.4.5 Ovarian Cancer

3.4.6 Pancreatic Cancer

3.4.7 Prostate Cancer

3.4.8 Rectal Cancer

4. Discussion

5. Conclusion

Declarations

References

Additional Declarations

Supplementary Files

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