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The core of the methods is that Bayesian modeling is divided into two stages characterized by a joint testing strategy based on the inherent attributes of this type of screening.
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With formal non-linear modeling, conversely, evidence of non-linearity can be tested directly, by a joint test on the non-linear terms.
Restricted cubic splines were used to model nonlinear covariates followed by a joint test to confirm the need for nonlinear terms (Harrell 2001).
We perform a joint test of normality where the joint hypothesis of s = 0 and k = 3 is tested.
Expect a joint test.
Pooled data obtained from parental families (R and S) and respective F1, F2 and BC crosses at the three diclofop-methyl doses tested were assessed by a joint scaling test.
This article presents a unified framework for handling this non-identifiability problem using two-step hierarchical informative prior on the sensitivities by two-stage Bayesian modeling with the characterized by joint testing strategies based on the inherent attribute of screening/diagnostic tests.
The emphasis is on factors that affect the accuracy, relevance and reliability of the test data delivered by the joint test.
In a two-staged design using 99,241 individuals of European ancestry, we identified 20 genome-wide significant (p ≤ 5 × 10−8) loci by using joint tests of the SNP main effect and SNP-age interaction.
Significant ORs for interaction terms are seen for smoking by wealth (joint test: 19.128, p<0.0001) and for chewing by wealth (joint test: 31.96, p<0.0001), education (joint test: 17.42, p<0.0001) and living environment (joint test: 157.008, p<0.0001).
The results from the interaction tests, supplemented by the joint tests where appropriate, will clarify the nature of the specific gene-disease associations.
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