Calibration of Transition Probabilities to Model Survival of Adjuvant Trastuzumab for Early Breast Cancer in Indonesia

This study addresses the calibration of transition probabilities in a Markov model to evaluate the survival outcomes of adjuvant trastuzumab versus standard chemotherapy for HER2-positive early breast cancer in Indonesia. The research aims to improve cost-effectiveness evaluations by using model calibration to adjust transition probabilities based on real-world evidence, which is often scarce in low- and middle-income countries. By comparing pre-calibrated and calibrated transition probabilities, the study provides a more accurate representation of survival outcomes, which can inform health policy decisions in Indonesia, where the high cost of trastuzumab poses a challenge for widespread access. The study underscores the importance of using real-world data for better decision-making in health technology assessments.