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Predictors were tested for crude association with the outcome before entering the preliminary primary effects model.
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Using our primary approach to estimating exposure (nearest monitor) and our primary health effects model, there were cross-sectional associations of CIMT with OC, EC, and sulfur, as well as with total PM2.5, but not with silicon.
We also extended the primary outcome model to explore the effect of patient preferences by including preference and an interaction term between preferred treatment and randomised treatment.
Owing to a priori assumptions about the likelihood for heterogeneity between primary studies, the random effects model of DerSimonian and Laird (1986) was used for pooled analyses.
For the analysis of the primary outcome, the mixed effects model will be refitted in a reduced PP population, excluding patients found to be ineligible at entry, and those not receiving the full randomised treatment up to and including the 8-week visit (whether due to discontinuation, exclusion or other reason for missing a randomised treatment in this period).
Repeating the primary analysis with a fixed effects model confirmed the overall results (relative risk 1.75, 1.59 to 1.92).
Primary analysis will adopt the random effects model in view of heterogeneity between trials.
Clinical researchers have often preferred to use a fixed effects model for the primary interpretation of a meta-analysis.
We assessed data for the primary outcome by using a random effects model, 10 to give a conservative estimate of the 95% confidence intervals.
To compare the results produced by the generalized estimating equations analyses (our primary analysis), a random coefficient (mixed effects model) and standard logistic regression analyses were conducted.
To assess the robustness of the results produced by the generalized estimating equations analyses (our primary analysis), a random coefficient (mixed effects model) and standard logistic regression analyses were conducted [ 23].
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