Work

A Bayesian Multi‐Factorial Design and Analysis for Estimating Combined Effects of Multiple Interventions in a Pragmatic Clinical Trial to Improve Dementia Care

Year: 2025

Type: article

Abstract: Factorial study designs can be important for understanding the effectiveness of interventions when multiple interventions are under investigation. In this design setting, a unit of randomization can be assigned to any combination of interventions. The rationale for taking this kind of approach can vary depending on the specific questions targeted by the research. These questions, in turn, have implications for the way in which the analyses will be conducted. The goal in this paper is to describe how we developed a factorial design along with a Bayesian analytic plan for a large cluster-randomized trial-the Emergency Departments LEading the transformation of Alzheimer's and Dementia care (ED-LEAD) study-focused on improving care for persons living with dementia.

Source: Statistics in Medicine

DOI: 10.1002/sim.70264

Authors: Keith S. Goldfeld, Corita R. Grudzen, Manish N. Shah, Abraham A. Brody, Joshua Chodosh, Rebecca Anthopolos

Institutions: New York University, Memorial Sloan Kettering Cancer Center, University of Wisconsin–Madison

Language: English

Topic: Statistical Methods in Clinical Trials

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: green

Funders: National Institutes of Health, National Institute on Aging, National Cancer Institute