Search

Assessment of benefit–risk for treatments is usually complex and involves trade-offs between multiple, often conflicting, preferences. Although discrete choice experiments are increasingly used in health outcomes research to assess trade-offs in preferences, their usage in benefit–risk assessment so far is fairly limited. This is primarily because of the high cognitive burden to assess multiple attributes and the requirement for a large pool of respondents. The hierarchical Bayes benefit–risk model using choice based conjoint that we propose drastically reduces the cognitive burden as each respondent only needs to evaluate a small fraction of preference questions, each of which compares a single pair of attributes. This method also leverages the Bayesian framework to borrow strength for analysis with a limited number of respondents. This article illustrates both a simulated experiment and a pilot experiment incorporating experts' preferences in oncology. Ultimately, patients are the most important voice in the benefit–risk balance. Therefore, we propose an augmented model to obtain a more precise estimate of benefit–risk preferences based on patients' characteristics. [ABSTRACT FROM AUTHOR]

The article focuses on a study regarding children with chronic myeloid leukemia (CML) and long term of effect of tyrosine kinase inhibitor (TKI) therapy and hematopoietic stem cell transplantation (HSCT). Topics discussed include significantly less number of percentage of CML in the pediatric population, introduction of the TKI imatinib by the U.S. in 1988, development of short term TKI toxicity, and improvement in overall survival of patients.