Sentence examples for causal analytic from inspiring English sources

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The article summarizes considerable advances in terms of number of studies, their scope, the degree of methodological sophistication, and their linkages to the allied disciplines and points to deficits with regard to longitudinal and causal analytic epidemiological studies.

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Availability: The causal analytics tools 'Upstream Regulator Analysis', 'Mechanistic Networks', 'Causal Network Analysis' and 'Downstream Effects Analysis' are implemented and available within Ingenuity Pathway Analysis (IPA, http://www.ingenuity.com).com

He argues that diseases are incapacities that explain symptoms in causal-analytic terms.

To test for a causal relationship between analytical thinking and religious disbelief, the researchers devised four different ways to promote analytic thinking and then surveyed the students to see if their religious disbelief had increased by the interventions that boosted critical thinking.

If Aristotle is following the method described in the Analytics, these causal explanations should at the same time point us to essential definitions of what it is to be a windpipe or to be viviparous.

Given the complexity and size of neuroimaging data, simple reproducible data-analytic methods, causal thinking, data exploration, hypothesis confirmation and careful design of experiments will become increasingly important, or, in other words, will require the expertise of biostatisticians!

First, the study was retrospective and therefore there was missing data and MRSA isolates, thus compromising the analytic results and causal inference.

Statistical analysis will be conducted according to a conceptual framework that defines a proposed causal pathway and the complex analytic approach to be used has been described in detail in a companion paper [ 18].

Complier-average causal effect (CACE) modelling is an analytic approach that provides a robust estimate of the treatment effect amongst compliant participants [ 1].

Nevertheless, ecologic study designs are helpful in identifying the extent of a public health problem, possible causal associations, and the need for analytic epidemiologic investigation (Merril and Timmreck 2006).

Second, although observational study designs preclude causal inference, our use of sophisticated analytic approaches to adjust for confounding and right-censoring improves confidence in the results over standard analytic methods that deal less directly with time-dependent confounding or selection bias (23, 25).

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