A Bayesian Multi-Factorial Design and Analysis for Estimating Combined Effects of Multiple Interventions in a Pragmatic Clinical Trial to Improve Dementia Care.
IF 1.8 4区 医学Q3 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Keith S Goldfeld, Corita R Grudzen, Manish N Shah, Abraham A Brody, Joshua Chodosh, Rebecca Anthopolos
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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.
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The journal aims to influence practice in medicine and its associated sciences through the publication of papers on statistical and other quantitative methods. Papers will explain new methods and demonstrate their application, preferably through a substantive, real, motivating example or a comprehensive evaluation based on an illustrative example. Alternatively, papers will report on case-studies where creative use or technical generalizations of established methodology is directed towards a substantive application. Reviews of, and tutorials on, general topics relevant to the application of statistics to medicine will also be published. The main criteria for publication are appropriateness of the statistical methods to a particular medical problem and clarity of exposition. Papers with primarily mathematical content will be excluded. The journal aims to enhance communication between statisticians, clinicians and medical researchers.
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