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Compositional data comprise the parts of some whole, for which all parts sum to that whole. They are prevalent in many epidemiological contexts. Although many of the challenges associated with analysing compositional data have been discussed previously, we do so within a formal causal framework by utilizing directed acyclic graphs (DAGs).
This article was published in the following journal.
Name: International journal of epidemiology
We start by proposing a causal induction model that incorporates symmetry bias. This model has two parameters that control the strength of symmetry bias and includes conditional probability and conven...
The relationship between lipids and the risk of fracture is currently controversial and whether such association is causal remains elusive.
Associations between putative risk factors and psychiatric and substance use disorders are widespread in the literature. Basing prevention efforts on such findings is hazardous. Applying causal infere...
Latent class analysis (LCA) has been effectively used to cluster multiple survey items. However, causal inference with an exposure variable, identified by an LCA model, is challenging because (1) the ...
We assessed whether an artifact's design can facilitate recognition of abstract causal rules. In Experiment 1, 152 three-year-olds were presented with evidence consistent with a relational rule (i.e.,...
The interpersonal problems of adolescents with ADHD may be the most debilitating aspect of their psychopathologic behaviour. This being said, the investigators still do not have a clear un...
Recent adolescent-based research shows that inference making improves across grades 6-12, uniquely accounts for variance in sentence- and passage-level comprehension, and that individual d...
The best treatment plan for Juvenile Idiopathic Arthritis (JIA) is often complicated. Patients and clinicians often don't know what is the best treatment strategy for a given patient at a ...
In this study, a retrospective analysis will be performed on collected data of 14 patients with Failed Back Surgery Syndrome, treated with Spinal cord stimulation. In separate studies, fM...
This project will examine how virtual reality treatment that provides users with the alternate perspective of a virtual interpersonal interaction impacts psychological and neurobiological ...
Systematic gathering of data for a particular purpose from various sources, including questionnaires, interviews, observation, existing records, and electronic devices. The process is usually preliminary to statistical analysis of the data.
Analysis based on the mathematical function first formulated by Jean-Baptiste-Joseph Fourier in 1807. The function, known as the Fourier transform, describes the sinusoidal pattern of any fluctuating pattern in the physical world in terms of its amplitude and its phase. It has broad applications in biomedicine, e.g., analysis of the x-ray crystallography data pivotal in identifying the double helical nature of DNA and in analysis of other molecules, including viruses, and the modified back-projection algorithm universally used in computerized tomography imaging, etc. (From Segen, The Dictionary of Modern Medicine, 1992)
Signal and data processing method that uses decomposition of wavelets to approximate, estimate, or compress signals with finite time and frequency domains. It represents a signal or data in terms of a fast decaying wavelet series from the original prototype wavelet, called the mother wavelet. This mathematical algorithm has been adopted widely in biomedical disciplines for data and signal processing in noise removal and audio/image compression (e.g., EEG and MRI).
The statistical manipulation of hierarchically and non-hierarchically nested data. It includes clustered data, such as a sample of subjects within a group of schools. Prevalent in the social, behavioral sciences, and biomedical sciences, both linear and nonlinear regression models are applied.
Information application based on a variety of coding methods to minimize the amount of data to be stored, retrieved, or transmitted. Data compression can be applied to various forms of data, such as images and signals. It is used to reduce costs and increase efficiency in the maintenance of large volumes of data.