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The significance of any differences in dichotomous data (OMERACT-OARSI treatment responders, proportion of patients reporting sufficient leisure time physical activity, number of adverse events) will be tested using generalised estimating equations or linear mixed model.
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Serial change of GFRs was tested using generalised estimating equation.
These measures of association were tested using generalised estimating equation analysis adjusting for within-patient correlation, and also other factors (age, gender, disease duration, baseline tender joint count, swollen joint count, ESR joint localisation (eg, PIP, MCP, wrists, MTP) and baseline structural damage).
Differences in quantitative variables (such as intakes) were tested using the generalised linear model (PROC GLM) adjusted for energy (ANCOVA) or age and sex (multivariate ANCOVA).
To test the effect of each factor and to control the hospital-clustering effect (Simpson paradox), all factors were tested using hierarchical generalised linear models in the four groups (Groups I IV).
The effect of study design and country income level (low middle or high) on the estimates was tested using logistic regression fitted by generalised estimating equations with an unstructured covariance matrix in Proc Genmod.
Homogeneity between studies was tested using the Mantel–Haenszel method with a generalised linear model.
Differences among the treatment arms were tested using an extension of the log rank test and both Gehan's and Peto's generalised Wilcoxon tests as implemented in STATISTICA (Stat soft, 1998).
The variables of interest were entered into separate generalised gamma AFT models as categorical variables, and their association with mesothelioma latency was tested using the LR test.
Training effects were tested with generalised estimating equation (GEE) and multivariate analysis of variance (MANOVA).
The analysis will be undertaken using generalised estimating equations (GEE).
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