Sentence examples for test of effect from inspiring English sources

We have introduced an extension to both MDR and MDR-PDT: a test of effect modification that has much higher specificity than the previous method of hypothesis testing.

Some future directions of this work will include extending this test of effect modification to more sensitive methods for interaction detection, such as generalized estimating equations (GEE) [27], [28].

Hence, the effect of COPD on suicide risk interacted significantly with psychiatric history (test of effect difference: χ=47.55, p<0.001 after adjustment for sociodemographic variables).

On a more stringent test of effect modification by hospital site, this interaction between site and ability was only significant for appreciation (odds ratio for site×appreciation 0.16, P = 0.02; odds ratio for site×reasoning 1.2, P = 0.72).

a: χ 6 2 Matestpe@MTEF@5@5@+=feaafiart1ev1aaatCvAUfKttLearuWrP9MDH5MBPbIqV92AaeXatLxBI9gBaebbnrfifHhDYfgasaacH8akY=wiFfYdH8Gipec8Eeeu0xXdbba9frFj0=ofFfeffectd9vqai=hGuQ8kuc9pgc9s8qqaq=dirpe0xb9q8qiLsFr0=vr0=vr0dc8meaabaqaciaacaGaaeqabaqabeGadaaakeaacqaHhpWydaqhaaWcbaGaeGofaydabaGaeGOmaidaaaaa@307C@ tesocialeffect of social network variable; values > 12.59 significant at P < 0.05.

Data are presented for those completing the 1-year intervention per protocol, and the primary test of effect was univariate ANCOVA analysis (intervention group as fixed factor [flavonoid/placebo] versus change in variable [pre (0 mol/L)/post (12 mol/L)], with baseline value of variable as covariate).

However, very few studies focused on the local test of effects of adhesive thickness on the interfacial traction-separation laws.

Finally, as a formal test of effect-modification, we test the significance of interactions between work stress and the dichotomised macro indicators (e.g. effort-reward imbalance * non-protective ALMP) in non-stratified analyses.

The homogeneous test of effects was performed using χ test, with P < 0.05 and I > 50%% indicating significant heterogeneity.

Existing interval regression methods used in binary trait analysis are adapted from quantitative trait analysis and the tests for regression coefficients are tests of effect, not detection.

Insufficient power also likely affected our ability to detect differences with time to event analyses and global tests of effect.