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Confidence intervals (CIs) at 95% were assessed by performing robust variance estimation to account for a cluster effect at the GP level and, consequently, also at the patient level.
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But meanwhile, we should rest assured, the UK has "a very robust export licensing process" (aka "one of the most robust arms export control regimes in the world" enabling the UK to perform "robust case-by-case assessments").
For all regressions, the dependencies due to repeated measurements were considered by performing a robust regression using a modified Huber/White/Sandwich estimator of variance [ 19].
The significant parameters were obtained by performing analysis of variance (ANOVA).
All regression statistics were performed using a robust variance to account for unmeasured ecologic and population differences.
As the number of segments with disease was not normally distributed, the comparison between the number and subtype of coronary artery segments with atherosclerosis in the two patients groups was performed by linear regression using robust variance estimates.
Highly significant parameters were determined by performing an Analysis of Variance (ANOVA).
Normal distribution was assessed by qq plots and variance homogeneity by robust variance test.
Small sample bias was addressed by scaling the robust variance estimator by n/n-p where n = number of clusters and p = number of parameters estimated.
†Results of univariable and multivariable Poisson regression adjusted for household clustering by using robust variance estimates.
In contrast, design-based analytical approach accounts for stratification and clustering by computing robust variance estimates.
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