Bayesian hierarchical piecewise regression models: a tool to detect trajectory divergence between groups in long-term observational studies

Bayesian hierarchical piecewise regression models: a tool to detect trajectory divergence between groups in long-term observational studies

BACKGROUND Bayesian hierarchical piecewise regression (BHPR) modeling has not been previously formulated to detect and characterise the mechanism of trajectory divergence between groups of participants that have longitudinal responses with distinct developmental phases. These models are useful when participants in a prospective cohort study are grouped according to a distal dichotomous health o...

Bayesian hierarchical models in ecological studies

The aim of this article is to set out a generic methodological framework for combining aggregated and individual level data for ecological inference and to illustrate some of the benefits and difficulties by simulations and on a case

Estimating mono- and bi-phasic regression parameters using a mixture piecewise linear Bayesian hierarchical model

The dynamics of tumor burden, secreted proteins or other biomarkers over time, is often used to evaluate the effectiveness of therapy and to predict outcomes for patients. Many methods have been proposed to investigate longitudinal trends to better characterize patients and to understand disease progression. However, most approaches assume a homogeneous patient population and a uniform response...

Quality Reporting of Multivariable Regression Models in Observational Studies

Controlling for confounders is a crucial step in analytical observational studies, and multivariable models are widely used as statistical adjustment techniques. However, the validation of the assumptions of the multivariable regression models (MRMs) should be made clear in scientific reporting. The objective of this study is to review the quality of statistical reporting of the most commonly u...

Bayesian Hierarchical Models 1 Running head: BAYESIAN HIERARCHICAL MODELS Bayesian Hierarchical Models