# Comparative Cost Evaluation of Managed Entry Agreement Techniques Using Real-World Data from High-Cost Anticancer Drugs in Thailand

**Authors:** Piyapat Owat, Chaoncin Sooksriwong, Hataiwan Ratanabunjerdkul, Tuangrat Phodha

PMC · DOI: 10.3390/jmahp14010017 · 2026-03-20

## TL;DR

This paper evaluates different cost-saving strategies for high-cost anticancer drugs in Thailand using real-world data to determine the most effective approaches.

## Contribution

The study introduces a framework for comparing the cost-saving performance of five MEA techniques using real-world data in a Thai healthcare context.

## Key findings

- Free initiation treatment and conditional treatment continuation showed the highest cost savings.
- Pay-by-result had the lowest cost-saving potential among the evaluated MEA techniques.
- MEA effectiveness depends on the dominant sources of drug-related uncertainty.

## Abstract

High-cost innovative anticancer drugs pose challenges for health systems in balancing timely patient access with long-term financial sustainability. In Thailand, reliance on Health Technology Assessment for reimbursement decisions may delay access, highlighting the potential role of Managed Entry Agreements (MEAs) as complementary policy instruments to manage uncertainty related to price, effectiveness, and use; however, MEA application remains limited and lacks an analytical framework for technique selection. This study used real-world data from Thammasat University Hospital to examine and compare the cost-saving performance of five MEA techniques—discount, free initiation treatment, utilization cap, conditional treatment continuation, and pay-by-result—across six high-cost anticancer drugs representing dominant uncertainty characteristics. Drug procurement costs were modeled over a 24-month horizon from the payer’s perspective, and one-way sensitivity analyses were conducted using ±10% variation in median progression-free survival. Free initiation treatment generated the highest cost savings across uncertainty types, followed by conditional treatment continuation, while utilization cap and discount produced more moderate savings. Pay-by-result demonstrated the lowest cost-saving potential. Sensitivity analyses confirmed the robustness of comparative rankings. Overall, the findings indicate that MEA performance varies according to dominant sources of drug-related uncertainty and support a more structured, context-appropriate approach to MEA selection to strengthen market access and value-based pricing in Thailand.

## Full-text entities

- **Genes:** ERBB2 (erb-b2 receptor tyrosine kinase 2) [NCBI Gene 2064] {aka CD340, HER-2, HER-2/neu, HER2, MLN 19, MLN-19}, ALK (ALK receptor tyrosine kinase) [NCBI Gene 238] {aka ALK1, CD246, NBLST3}, EGFR (epidermal growth factor receptor) [NCBI Gene 1956] {aka ERBB, ERBB1, ERRP, HER1, NISBD2, NNCIS}, NR4A1 (nuclear receptor subfamily 4 group A member 1) [NCBI Gene 3164] {aka GFRP1, HMR, N10, NAK-1, NGFIB, NP10}
- **Diseases:** injury to (MESH:D014947), death (MESH:D003643), NSCLC (MESH:D002289), stage IV disease (MESH:D007676), Solid Tumors (MESH:D009369), MEA (MESH:D018761), breast cancer (MESH:D001943)
- **Chemicals:** MEA (-), ribociclib (MESH:C000589651), ceritinib (MESH:C586847), afatinib (MESH:D000077716), palbociclib (MESH:C500026), pertuzumab (MESH:C485206), osimertinib (MESH:C000596361)
- **Species:** Homo sapiens (human, species) [taxon 9606]
- **Mutations:** T790M

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Source: https://tomesphere.com/paper/PMC13027474