Cost-effectiveness of chemotherapy versus targeted therapy in breast cancer: A retrospective analysis in tertiary care hospital

Abstract

Background

Breast cancer represents a significant economic burden globally, with treatment costs varying substantially between conventional chemotherapy and newer targeted therapies. Economic evaluation is essential for healthcare decision-making, particularly in resource-limited settings.

Objective

To evaluate the cost-effectiveness of targeted therapy versus chemotherapy in breast cancer treatment from a healthcare system perspective in an Indian tertiary care hospital setting.

Methods

A retrospective cost-effectiveness analysis was conducted comparing patients receiving chemotherapy versus targeted therapy for breast cancer. Direct medical costs were calculated including drug costs, hospitalization, and diagnostic services. Health outcomes were measured using quality-adjusted life years (QALYs) derived from patient-reported EQ-5D visual analogue scale (VAS) based utility values accrued during treatment and post-treatment progression-free survival. The incremental cost-effectiveness ratio (ICER) was calculated and compared against India's willingness-to-pay threshold based on per capita GDP.

Results

The analysis included two treatment groups: chemotherapy (mean cost: ₹52,173) and targeted therapy (mean cost: ₹1,044,113). QALYs achieved were significantly higher in the targeted therapy group (2.398 vs 1.083, p < 0.001). The ICER for targeted therapy was ₹754,264 per QALY gained, which falls below willingness-to-pay threshold of 2–3 times per capita GDP (₹806,428–₹1,209,642).

Conclusions

Despite substantially higher treatment costs, targeted therapy demonstrates cost-effectiveness compared to chemotherapy when considering health outcomes. The ICER remains within acceptable thresholds for India, supporting targeted therapy as a cost-effective treatment option for appropriate breast cancer patients.

Keywords

Cost-effectiveness analysis breast cancer targeted therapy chemotherapy QALY (Quality adjusted life years)and pharmacoeconomics

Introduction

Breast cancer is the most common malignancy affecting women worldwide and remains a leading cause of cancer-related morbidity and mortality.¹ It accounts for a significant proportion of the global cancer burden, with increasing incidence rates observed in both developed and developing countries. Advances in early detection, diagnosis, and treatment have improved survival outcomes in many parts of the world, yet breast cancer continues to pose a major public health challenge. Beyond its clinical implications, the disease represents a considerable economic burden on healthcare systems due to the costs associated with diagnosis, treatment, follow-up care, and management of long-term complications.¹

The treatment landscape for breast cancer has undergone a paradigm shift over the past two decades, largely driven by the introduction of targeted therapies. Unlike conventional chemotherapy, which non-selectively affects both cancerous and normal cells, targeted therapies act on specific molecular pathways implicated in tumor growth and progression. This mechanism of action has translated into improved treatment efficacy, better tolerability, and enhanced survival outcomes for certain subtypes of breast cancer, particularly HER2-positive and hormone receptor–positive disease.2,3 However, these clinical advantages come at a substantially higher cost compared to traditional chemotherapy regimens, thereby raising important questions about affordability and accessibility, especially in low- and middle-income countries (LMICs).^2,3

In resource-constrained healthcare settings such as India, decision-makers face the difficult task of balancing clinical benefit against financial feasibility.⁴ While targeted therapies may significantly extend survival and improve quality of life for patients, their high costs impose a strain on both public healthcare systems and individual households, many of which rely on out-of-pocket payments for medical expenses. Consequently, healthcare providers and policymakers are often forced to choose between treatments that offer superior clinical outcomes but require substantial financial investment versus more affordable conventional alternatives that may deliver comparatively inferior outcomes.⁴ Such dilemmas underscore the need for systematic and evidence-based frameworks to guide treatment decision-making.

Pharmacoeconomic evaluation provides a robust and structured approach to address these concerns by comparing the value of different treatment strategies in terms of both costs and health outcomes.⁵ These evaluations are critical in identifying therapies that maximize health benefits within the constraints of limited healthcare budgets. By integrating economic analysis with clinical data, pharmacoeconomics helps stakeholders—including clinicians, hospital administrators, insurers, and policymakers—allocate resources more efficiently and ensure equitable access to effective care.⁵ In the context of breast cancer, where treatment choices are often complex and costly, pharmacoeconomic assessments are particularly relevant.

The economic burden of breast cancer extends far beyond direct medical expenses such as drug acquisition, hospitalizations, diagnostic procedures, and supportive care.⁶ Indirect costs, including productivity losses due to morbidity and premature mortality, as well as caregiver burden, further compound the overall economic impact. Moreover, intangible costs such as psychological distress, reduced social functioning, and impairment of quality of life represent additional dimensions of disease burden that are difficult to quantify but remain highly significant.⁶ Given this multidimensional nature of costs, comprehensive evaluation of treatment strategies requires not only clinical efficacy data but also measures that capture both survival duration and quality of life.

Quality-adjusted life years (QALYs) serve as a standardized metric to assess treatment value by integrating these dimensions. The QALY framework incorporates both the length of survival and the health-related quality of life experienced during that period, thereby enabling meaningful comparisons between different therapeutic options and across various disease areas.⁷ Cost-utility analysis, which expresses outcomes in terms of cost per QALY gained, is increasingly recognized as the gold standard in health economic evaluation. This approach facilitates transparent assessment of whether the additional costs of innovative treatments, such as targeted therapies, are justified by corresponding gains in patient survival and quality of life.

Previous pharmacoeconomic studies have explored the cost-effectiveness of targeted therapies in breast cancer, reporting a wide range of incremental cost-effectiveness ratios (ICERs) across different healthcare contexts. These variations are influenced by multiple factors, including patient characteristics, disease subtype, treatment line, healthcare system perspective (payer, societal, or provider), and local economic conditions. For instance, what may be considered cost-effective in high-income countries with greater healthcare resources and higher willingness-to-pay thresholds may not be economically viable in LMICs, where budgetary constraints are more pronounced.^8,9 Furthermore, much of the existing evidence is derived from Western populations, and there remains a paucity of real-world data addressing cost-effectiveness in the Indian healthcare setting.

In India, the burden of breast cancer is rising steadily, with increasing incidence among younger women and a significant proportion of cases diagnosed at advanced stages. Limited public healthcare financing, high reliance on out-of-pocket expenditure, and disparities in access to advanced treatments further complicate disease management. Despite the availability of targeted therapies, their uptake in routine practice is often restricted due to financial barriers. Consequently, there is an urgent need for context-specific economic evaluations that reflect local treatment practices, cost structures, and patient outcomes.

This study aims to address this evidence gap by evaluating the cost-effectiveness of targeted therapy versus conventional chemotherapy in the management of breast cancer using real-world data from an Indian tertiary care hospital. By employing a cost-utility framework and measuring outcomes in terms of QALYs, this study seeks to provide robust evidence that can inform clinical and policy decision-making in resource-limited settings. Ultimately, the findings are intended to guide healthcare stakeholders in balancing the goals of maximizing patient outcomes and ensuring the sustainability of healthcare expenditures in India.

Methods

Study design and setting

A retrospective cost-effectiveness analysis was conducted at a tertiary care hospital in Hyderabad, Telangana, India. The study compared patients receiving chemotherapy versus targeted therapy for breast cancer treatment over the study period.

Study population

The analysis included adult patients diagnosed with breast cancer who received either conventional chemotherapy or targeted therapy during the study period. Patients were categorized into two groups based on their primary treatment modality: chemotherapy group and targeted therapy group. Only patients with complete treatment cost data and documented EQ-5D visual analogue scale (VAS) scores recorded during active treatment and after completion of treatment were included in the analysis.

Treatment Classification

Patients were categorized into two mutually exclusive treatment groups based on the primary systemic therapy received.

Chemotherapy group: Patients received conventional cytotoxic chemotherapy regimens, including cyclophosphamide, docetaxel, doxorubicin, fluorouracil, paclitaxel, carboplatin, and methotrexate, administered according to institutional protocols.

Targeted therapy group: Patients received targeted anticancer agents including trastuzumab, pertuzumab, lapatinib, and capecitabine, administered as standalone targeted therapy regimens.

Patients receiving combination regimens involving both chemotherapy and targeted agents were not included in the analysis.

This classification reflects routine real-world treatment practices in the study setting and was adopted to facilitate a clear comparison of treatment strategies.

Perspective and time horizon

The economic evaluation was conducted from a healthcare system perspective, considering direct medical costs incurred during breast cancer treatment. The time horizon for cost and outcome estimation was restricted to the observed progression-free survival (PFS) period.

The median duration of follow-up, corresponding to the progression-free survival period, was 33 months (range: 33–36 months) in the targeted therapy group and 18 months (range: 18–24 months) in the chemotherapy group. No extrapolation beyond the observed PFS period was performed.

Cost analysis

Direct medical costs were comprehensively calculated for each treatment group, including:

Pharmaceutical costs (chemotherapy drugs and targeted therapy agents)

Hospitalization expenses

Diagnostic and monitoring services

Administration costs

Management of adverse events

All costs were expressed in Indian Rupees (₹) and calculated as per-patient averages for each treatment group. Cost data were extracted from hospital billing records and pharmacy dispensing records.

Health outcome measurement

Health outcomes were measured using quality-adjusted life years (QALYs). Health-related quality of life was measured using the EQ-5D Visual Analogue Scale (VAS), which records patients’ self-rated health on a scale from 0 (worst imaginable health state) to 100 (best imaginable health state). EQ-5D VAS scores were routinely documented in patient medical records and retrospectively extracted for analysis.

Utility values were derived from EQ-5D VAS scores using a linear transformation approach to obtain values on a 0–1 scale. QALYs were estimated over the progression-free survival period using a two-phase area-under-the-curve approach. Utility values measured during active treatment were applied to the treatment duration (typically 6–8 cycles), while utility values measured after completion of treatment were applied to the remaining progression-free survival period. Total QALYs were calculated as the sum of utilities accrued during the treatment and post-treatment progression-free phases.

Formula:

\begin{aligned} QALYs = & (Utility score \times Time during treatment (in years)) \\ + (Utility score \times Time after treatment (in years)) \end{aligned}

Utility Estimation Assumptions

As EQ-5D descriptive system responses required for tariff-based utility estimation were not available, population-specific preference weights, including Indian value sets, could not be applied. Utility estimation was therefore based on transformed EQ-5D VAS scores. QALY estimation was restricted to the progression-free survival period, and post-progression or lifetime utilities were not considered.

Cost-Effectiveness analysis

The incremental cost-effectiveness ratio (ICER) was calculated using the standard formula:

ICER = (Cost of targeted therapy – Cost of chemotherapy) / (QALYs of targeted therapy – QALYs of chemotherapy)

The ICER represents the additional cost per additional QALY gained when using targeted therapy instead of chemotherapy.

Willingness-to-pay threshold

The cost-effectiveness threshold was established based on Hyderabad per capita GDP for Hyderabad (₹403,214). Following WHO recommendations for low- and middle-income countries, interventions with ICERs below 2–3 times per capita GDP were considered cost-effective for critical healthcare interventions such as cancer treatment.

Statistical analysis

Statistical comparisons between treatment groups were performed using Student's t-test for continuous variables. Cost and QALY differences between groups were tested for statistical significance. A p-value of <0.05 was considered statistically significant. Variance analysis was conducted to assess the distribution of costs within each treatment group.

Ethical considerations

The study was conducted following ethical guidelines for retrospective research using anonymized patient data. Patient confidentiality was maintained throughout the analysis.

Results

Age

The patients are divided into categories as per their age and patients were further examined within each age group. Of note, the highest age group between 40–49 years had 64 patients, which is significantly high. The lowest is 80–89 age group represented in Table 1. The age-wise distribution of patients in both treatement groups is shown graphically in Figure 1.

Cancer varieties

In this study, Patients with breast carcinoma come in around 10 different varieties, with medullary carcinoma accounting for the biggest percentage (12.69%). The remainder consist of “Ductal invasive” (10.38%), “Mucinous carcinoma” (10.76%), and “Papillary carcinoma” (11.15%), with “Tubular carcinoma” (6.53%) making up the smallest percentage (Table 2). The distribution of patients according to the type of cancer is illustrated in Figure 2. Seperately targeted therapy group patients distribution is illustrated in Figure 3, chemotherapy group patients distribution is illustrated in Figure 4.

Cost analysis

The cost analysis revealed substantial differences between treatment groups. The chemotherapy group had a mean treatment cost of ₹52,173 per patient, while the targeted therapy group incurred significantly higher costs with a mean of ₹1,044,113 per patient shown in Table 3. This represents a 20-fold difference in treatment costs between the two modalities.

Table 1.

Patients distribution based on age.

Age group	Patients count	%	Patients of (Targeted therapy)	Patients of (chemotherapy)
30–39	37	14.23076923	9	28
40–49	64	24.61538462	37	27
50–59	52	20	29	23
60–69	47	18.07692308	26	21
70–79	50	19.23076923	23	27
80–89	10	3.846153846	6	4
	260	100	130	130

Statistical analysis using one-way Student's t-test demonstrated a highly significant difference in costs between groups (p = 8.16 × 10⁻⁴⁸). The variance analysis showed considerably higher cost variability in the targeted therapy group (variance: 2.42 × 10¹¹) compared to the chemotherapy group (variance: 9.59 × 10⁸).

Quality-adjusted life years analysis

Health outcome analysis revealed superior QALY gains in the targeted therapy group compared to chemotherapy. The targeted therapy group achieved a mean of 2.398 QALYs per patient, while the chemotherapy group achieved 1.083 QALYs per patient represented in Table 4.

Table 2.

Patients distribution according to cancer type.

Cancer type	Patients count	Patient %	Patients from targeted therapy	Patients with chemotherapy
Ductal carcinoma	26	10	11	15
Ductal In Situ	25	9.615384615	20	5
Ductal Invasive	27	10.38461538	14	13
Lobular carcinoma	25	9.615384615	12	13
Lobular In situ	26	10	17	9
Lobular Invasive	24	9.230769231	9	15
Medullary carcinoma	33	12.69230769	12	21
Mucinous carcinoma	28	10.76923077	12	16
Papillary carcinoma	29	11.15384615	13	16
Tubular carcinoma	17	6.538461538	10	7
	260	100	130	130

Table 3.

Treatment costs by group.

Treatment group	Mean cost (₹)	Standard deviation
Chemotherapy	52,173	30,964
Targeted Therapy	1,044,113	492,139

Table 4.

Health outcomes by treatment group.

Treatment group	Mean QALYs	QALY difference
Chemotherapy	1.083	-
Targeted Therapy	2.398	+1.315

Table 5.

Cost-Effectiveness results.

Parameter	Value
Incremental Cost (₹)	991,940
Incremental QALYs	1.315
ICER (₹/QALY)	754,264

Statistical testing confirmed a highly significant difference in QALY gains between treatment groups (p = 9.66 × 10⁻¹¹⁷), with the null hypothesis rejected, supporting the alternative hypothesis that targeted therapy patients achieved superior health outcomes.

Cost-effectiveness analysis

The incremental cost-effectiveness ratio (ICER) for targeted therapy versus chemotherapy was calculated as ₹754,264 per QALY gained. This was derived from the incremental cost of ₹991,940 and incremental effectiveness of 1.315 QALYs (Table 5). Graphically illustrated in Figure 5.

Threshold analysis

The cost-effectiveness evaluation was assessed against India-specific willingness-to-pay thresholds:

2× per capita GDP threshold: ₹806,428 per QALY

3× per capita GDP threshold: ₹1,209,642 per QALY

The calculated ICER of ₹754,264.00 per QALY falls below both threshold values, indicating that targeted therapy is cost-effective compared to chemotherapy in the Indian healthcare context.

Sensitivity analysis

Based on the “Sensitivity Analysis”, the study assessed how cost-effectiveness results vary with a ± 20% change in the total cost of both chemotherapy and targeted therapy. Even after accounting for these cost variations, targeted therapy consistently yielded higher QALYs compared to chemotherapy, reaffirming its clinical effectiveness. Although the total cost of targeted therapy is significantly higher, its incremental cost-effectiveness ratios (ICERs) remain within a justifiable range given the substantial QALY gains. This suggests that the cost-utility advantage of targeted therapy over chemotherapy is robust and remains favorable under varying cost conditions. Illustrated in Figure 6.

Discussion

This pharmacoeconomic evaluation provides compelling evidence for the cost-effectiveness of targeted therapy versus conventional chemotherapy in breast cancer treatment within the Indian healthcare context. Our findings demonstrate that despite the substantial initial cost differential of approximately 20-fold, targeted therapy achieves favorable cost-effectiveness when evaluated through comprehensive health outcome measures. The calculated incremental cost-effectiveness ratio (ICER) of ₹754,264 per quality-adjusted life-year (QALY) gained falls well within established cost-effectiveness thresholds for India,¹⁰ aligning with the cost-effectiveness threshold range that suggests interventions costing less than three times the national annual GDP per capita represent good value for money.¹⁰ The acceptance of interventions with ICERs below this threshold supports the economic viability of targeted therapy implementation. A key strength of this study is the clear separation of chemotherapy-only and targeted therapy-only treatment strategies, avoiding overlap between treatment groups. In addition, the use of patient-reported EQ-5D VAS data collected during routine clinical practice allowed for a realistic estimation of utility values and health outcomes within the progression-free period. Additionally, treatment and post-treatment progression-free QALY estimates using separate utility values allowed for a more detailed and clinically meaningful depiction of the patient experience, particularly improvement in quality of life after the end of treatment.

Our results are consistent with several international cost-effectiveness analyses of targeted therapies in breast cancer. A study conducted in India by Prinja S et al., evaluates the incremental cost per QALY gained with adjuvant trastuzumab among patients with nonmetastatic breast cancer in India using a Markov model calibrated with Indian cancer registry data, making it the appropriate and accurate source for your article.¹¹ Our analysis demonstrates that targeted therapies can achieve cost-effectiveness despite higher acquisition costs, with our ICER being more favorable than some international studies. However, our findings contrast with some metastatic breast cancer studies, such as the analysis by Zhan et al., which reported ICERs exceeding $336,000 per QALY for trastuzumab deruxtecan in Chinese healthcare settings.¹² The superior health outcomes associated with targeted therapy justify the higher upfront costs through several mechanisms, consistent with international evidence from systematic reviews of HER2-targeted interventions.¹³ These clinical benefits translate into economic value through reduced long-term healthcare utilization, decreased complications, and improved quality of life measures.

The cost-effectiveness profile observed in our study compares favorably with recent analyses of targeted therapies in breast cancer. While some studies, particularly those evaluating pertuzumab combinations, have reported ICERs exceeding conventional thresholds, with one Canadian study reporting ICERs of $436,679 per QALY,¹⁴ our analysis demonstrates that appropriate patient selection and treatment optimization can achieve cost-effective outcomes. This aligns with findings from a Brazilian cost-effectiveness analysis that reported an ICER of US$19,599 per QALY for adjuvant trastuzumab, which was considered cost-effective using Brazilian GDP-based thresholds.¹⁵ The substantial variation in ICERs across international studies highlights the importance of conducting country-specific economic evaluations that reflect local healthcare costs, treatment patterns, and outcome measures. A systematic review by Diaby and Almutairi emphasized that heterogeneous evidence from cost-effectiveness studies cautions against cross-country comparisons and supports the production of cost-effectiveness analyses for local rather than global decision-making.¹³

The economic evidence generated supports the inclusion of targeted therapies in treatment guidelines and reimbursement frameworks within the Indian healthcare system. This recommendation aligns with the draft National Health Policy of India's emphasis on evidence-based healthcare decision-making and the development of technology assessment capabilities modeled after international best practices.¹⁰ The favorable cost-effectiveness profile strengthens the clinical rationale for utilizing these treatments in appropriate patient populations, particularly those with favorable biomarker profiles. The findings also support risk-sharing arrangements and value-based pricing strategies that could further optimize the cost-effectiveness profile of targeted therapies. A systematic review of cost-effectiveness analyses in developing countries noted that many breast cancer drugs were not cost-effective in developing countries, emphasizing the need for setting-specific evaluations.¹⁶

Several limitations warrant consideration in interpreting these findings. The analysis was conducted using a specific patient population and treatment protocols, which may limit generalizability to broader clinical contexts. Additionally, the evaluation focused on direct medical costs and may not fully capture indirect costs and societal impacts. QALYs were estimated only for the progression-free period and did not incorporate post-progression or long-term survival utilities. Additionally, utility values were derived from EQ-5D VAS scores rather than tariff-based preference weights, which may limit comparability with lifetime model-based economic evaluations. Future research should explore the cost-effectiveness of newer targeted agents, combination therapies, and biosimilar alternatives as they become available in the Indian market. The evolving landscape of precision medicine and biomarker-driven treatment selection presents opportunities for further optimization of cost-effectiveness ratios through improved patient stratification and personalized therapy approaches. This analysis provides robust evidence that targeted therapy represents a cost-effective treatment option for breast cancer patients in the Indian healthcare setting, despite higher acquisition costs compared to conventional chemotherapy. The ICER of ₹754,264 per QALY gained supports the economic value proposition of these interventions and their inclusion in formulary and reimbursement decisions, contributing to the growing body of pharmacoeconomic evidence supporting the adoption of precision medicine approaches in oncology care within resource-constrained healthcare systems.

Conclusions

This cost-effectiveness analysis demonstrates that targeted therapy, despite substantially higher upfront costs, provides superior value compared to chemotherapy in breast cancer treatment when evaluated from a healthcare system perspective. The ICER of ₹754,264 per QALY gained falls within India's acceptable cost-effectiveness thresholds, supporting targeted therapy as a cost-effective treatment option.

These findings provide important evidence for healthcare decision-makers, clinicians, and policymakers regarding the economic value of targeted therapies in breast cancer treatment. The results support the integration of targeted therapies into treatment protocols and insurance coverage decisions, particularly in resource-constrained settings where economic considerations are paramount.

The study contributes valuable Indian-specific pharmacoeconomic evidence to the growing body of literature on cancer treatment cost-effectiveness, supporting evidence-based healthcare resource allocation decisions.

Footnotes

Acknowledgments

I would like to thank the hospital authorities, oncologists, and medical personnel who generously granted access to patient files and provided relevant insights, which were essential for the successful conduct of this study. Their willingness to collaborate and aid in the primary data collection has immensely contributed to the success of this research.

Furthermore, I would want to express my appreciation to the supporting staff as well as the administrative staff at Malla Reddy Pharmacy College and in the hospital, who contributed in different ways to make this work successful. I value their assistance in getting these materials and working effectively as coordinators.

Finally, my appreciation goes out to my family, friends, and colleagues who stood by me and offered immeasurable support through this strenuous journey. Their expectation from me never wavered, and their confidence in me during the rough times was very encouraging.

ORCID iDs

Naresh Kunta

Sriharsha Varakala

Vaishnavi Devi

Yogyatha Nemmani

Jagadeeshwar Ksheerasagar

Tulja Rani Gampa

Ethical considerations

This research study was conducted retrospectively from data obtained for clinical purposes. We consulted extensively with the IEC/IRB who determined that our study did not need ethical approval. An IEC/IRB official waiver of ethical approval was granted from the IEC.

Consent to participate

Not applicable

Consent to publication

Not applicable

Author contributions

N.K.: Conceptualization, Supervision, Investigation, Project Administration.

S.V.: Conceptualization, Methodology, Formal Analysis, Data Curation, writing – Original Draft, Writing – Review & Editing.

V.D.: Data Curation, Writing – Original Draft.

Y.N.: Data Curation, Writing – Original Draft.

J.K.: Supervision, Writing – Review & Editing.

T.R.G.: Supervision, Administrative Support.

S.J.: Resources, Administrative Support.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data availability statement

The data supporting the conclusions of this article are available from the corresponding author upon reasonable request, subject to institutional ethical approval.

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