A Bayesian latent class approach to causal inference with longitudinal data.

ABSTRACT: Bayesian methods are becoming increasingly in demand in clinical and public health comparative effectiveness research. Limited literature has explored parametric Bayesian causal approaches to handle time-dependent treatment and time-dependent covariates. In this article, building on to the work on Bayesian g-computation, we propose a fully Bayesian causal approach, implemented using latent confounder classes which represent the patient's disease and health status. Our setting is suitable when the latent class represents a true disease state that the physician is able to infer without misclassification based on manifest variables. We consider a causal effect that is confounded by the visit-specific latent class in a longitudinal setting and formulate the joint likelihood of the treatment, o

SUBMITTER: Liu K 

PROVIDER: S-EPMC11800708 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature