Bayesian graphical modeling for heterogeneous causal effects

• Published in: Statistics in medicine Vol. 42; no. 1; pp. 15 - 32
• Main Authors: Castelletti, Federico, Consonni, Guido
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 15.01.2023; Wiley Subscription Services, Inc
• Subjects: Bayes Theorem; Blood cancer; Causality; Cell cycle; Chemotherapy; directed acyclic graph; Dirichlet process mixture; Humans; Leukemia, Myeloid, Acute - etiology; Leukemia, Myeloid, Acute - genetics; Normal Distribution; personalized treatment; subject‐specific graph; tumor heterogeneity; directed acyclic graph; Dirichlet process mixture; subject-specific graph; personalized treatment; tumor heterogeneity
• ISSN: 0277-6715; 1097-0258; 1097-0258
• DOI: 10.1002/sim.9599

There is a growing interest in current medical research to develop personalized treatments using a molecular‐based approach. The broad goal is to implement a more precise and targeted decision‐making process, relative to traditional treatments based primarily on clinical diagnoses. Specifically, we consider patients affected by Acute Myeloid Leukemia (AML), an hematological cancer characterized by uncontrolled proliferation of hematopoietic stem cells in the bone marrow. Because AML responds poorly to chemotherapeutic treatments, the development of targeted therapies is essential to improve patients' prospects. In particular, the dataset we analyze contains the levels of proteins involved in cell cycle regulation and linked to the progression of the disease. We evaluate treatment effects within a causal framework represented by a Directed Acyclic Graph (DAG) model, whose vertices are the protein levels in the network. A major obstacle in implementing the above program is represented by individual heterogeneity. We address this issue through a Dirichlet Process (DP) mixture of Gaussian DAG‐models where both the graphical structure as well as the allied model parameters are regarded as uncertain. Our procedure determines a clustering structure of the units reflecting the underlying heterogeneity, and produces subject‐specific estimates of causal effects based on Bayesian Model Averaging (BMA). With reference to the AML dataset, we identify different effects of protein regulation among individuals; moreover, our method clusters patients into groups that exhibit only mild similarities with traditional categories based on morphological features.