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†Results of univariable and multivariable Poisson regression adjusted for household clustering by using robust variance estimates.
Risk factors for perinatal mortality were analysed by Poisson regression, by using robust variance estimates and expressed as RR.
Such comparisons took into account intra-family correlation between a survivor and his or her sibling by using robust variance estimates from generalised estimating equations.
We accounted for the clustering of patients within GPs by using robust variance estimates [ 22] in both univariate and multivariate models.
We allowed for the fact that several individuals may come from the same family by using robust variance estimation [ 17, 18].
We then tested the presence, strength, and independence of associations between each of the hospital characteristics and the CIT, automation, and usability scores using linear regression models, adjusting for the percentage of complete responses and accounting for possible within-hospital clustering of physician responses by using robust variance techniques.
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Multivariate analysis was carried out by Poisson regression, using robust variance estimates as appropriate for binary outcomes with high prevalence [ 25].
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.
Adjustments for clustering by practice were made using robust variance estimates.
PPRs) of unwanted pregnancy and associated 95%% confidence intervals were estimated by generalized linear models with log link function and Poisson family distribution using robust variance estimator.
Multiple events were allowed, accounting for dependence within individuals using robust variance estimates.
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