Work

A Generalized Interrupted Time Series Model for Assessing Complex Health Care Interventions

Year: 2022

Type: article

Source: Statistics in Biosciences

DOI: 10.1007/s12561-022-09346-6

Authors: Maricela Cruz, Hernando Ombao, Daniel L. Gillen

Institutions: Kaiser Permanente Washington Health Research Institute, King Abdullah University of Science and Technology, University of California, Irvine

Language: English

Topic: Advanced Causal Inference Techniques

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: green

Funders: National Science Foundation, National Institutes of Health, University of California, Irvine, National Science Foundation of Sri Lanka, National Institute on Aging, National Institute of Mental Health

Awards: G1247073731, Collaborative Research: Bayesian State-Space Models for Behavioral Time Series Data, The Alzheimer's Disease Research Center at the University of California, Irvine, Graduate Research Fellowship Program (GRFP), G5644055388, G5908980400, G6740654831, Collaborative Research: Studies on Signals and Images via the Fourier Transform, Scalable Bayesian Stochastic Process Models for Neural Data Analysis, Assessing the role of cerebrovascular brain injury and dysfunction in Alzheimer’s disease pathogenesis in the BEACoN Cohort, The Alzheimer's Disease Research Center at the University of California, Irvine

OpenAlex ID: W4281491666

FWCI: 0.369

Cites: 17

Cited by: 2

Related to: 10