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Medicine

October 1, 2026 · via Europe PMC

Cost-effectiveness of pembrolizumab plus chemoradiotherapy versus placebo plus chemoradiotherapy for high-risk locally advanced cervical cancer in Saudi Arabia: A model-based economic evaluation.

Almalki ZS, Alamer AA, Alhumaid SK, +5 more, Alanazi HH.

Journal ArticleOpen access

AI summary — for doctors, generated from the abstract only

In Saudi Arabia, adding pembrolizumab to chemoradiotherapy for high-risk locally advanced cervical cancer is likely cost-effective at about $82,626 per QALY.

What was studied

A partitioned survival model from the Saudi payer perspective using KEYNOTE-A18 efficacy, local costs and expert elicitation, projecting lifetime costs and QALYs.

Key findings

  • Gains of 3.51 life-years and 2.93 QALYs at an extra cost of $242,043
  • ICER was $82,626 per QALY
  • Probability of cost-effectiveness was 67% at $90,000 per QALY

Clinical relevance

It informs reimbursement discussions for an immunotherapy that improves survival in cervical cancer.

Who should care

General Medicine

Limitations

It is a model dependent on trial extrapolation and local cost estimates, with a 67% probability meaning meaningful uncertainty remains.

Pembrolizumab with chemoradiotherapy appears good value at current price, but price negotiation strengthens the case.

This is an AI-generated summary of the published abstract, not a clinical recommendation. It may not reflect the full text or subsequent updates — verify against the original source before acting on it.

Abstract

Background

Adding pembrolizumab to standard concurrent chemoradiotherapy (CCRT) has shown substantial survival benefits in newly diagnosed, high-risk locally advanced cervical cancer (LACC), but its economic value in Saudi Arabia has not been established. This study assessed the cost-effectiveness of pembrolizumab plus CCRT versus placebo plus CCRT as first-line treatment for high-risk LACC from the perspective of the Saudi Arabian healthcare payer.

Methods

A partitioned survival analysis model was developed to project lifetime costs and quality-adjusted life-years (QALYs) for a hypothetical cohort of women with high-risk LACC in the Saudi public healthcare sector, using resource use patterns consistent with major tertiary care and specialized cancer centers. Clinical efficacy data were obtained from the KEYNOTE-A18 trial. Costs were sourced from official Saudi price lists, local hospital data, and a formal Structured Expert Elicitation process, and were reported in 2025 United States dollars. Health state utilities were derived from the literature. The primary outcome was the incremental cost-effectiveness ratio. Future costs and QALYs were discounted at an annual rate of 3%. Comprehensive deterministic and probabilistic sensitivity analyses were conducted to assess uncertainty.

Results

Adding pembrolizumab to CCRT resulted in an incremental gain of 3.51 life-years and 2.93 QALYs at an additional cost of $242,043 compared with placebo plus CCRT. At its current list price, the regimen yielded an incremental cost-effectiveness ratio of $82,626 per QALY. At a willingness-to-pay threshold of $90,000 per QALY, the probability of the pembrolizumab regimen being cost-effective was 67%.

Conclusion

Pembrolizumab in combination with CCRT provides substantial clinical benefits in patients with high-risk LACC in Saudi Arabia. At the current price, this regimen is likely to be considered cost-effective. The analysis provides a strong, evidence-based rationale for significant price negotiations to ensure that the regimen delivers value to the Saudi healthcare system.

DOI

10.1097/md.0000000000050990

PMID

42826320

Research metadata (titles, abstracts, authors, publication dates) is sourced from Europe PMC, which aggregates content from PubMed/MEDLINE, PubMed Central, and other providers under the U.S. National Library of Medicine (NLM/NCBI). NLM does not claim copyright on abstracts; the original journal publishers or authors may hold rights to the full articles. AI-generated summaries are derived only from the abstract, never the full text, and are always labeled as AI-generated.

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