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Secondary objective was to assess 6-month prognosis in term of mortality and persistent renal dysfunction by a logistic model with inverse probability of treatment weighting, considering the treatment most received in the first 7 days.
In all analyses a random effects model with inverse variance weights were applied [ 19].
All analyses will be preformed by applying a random effects model with inverse variance weights [ 37].
Some of the parameter estimates were combined using a random effects model with inverse variance weights [[ 7], Sec.
We summarised results as the mean difference of continuous variables with 95% CIs and combined data by means of a random effects model with inverse variance weighting.
As we expected heterogeneity between studies, we pooled effect estimates using a random effects model with inverse variance weighting and Review Manager 5.1 software [ 25].
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For the concatenated unpartitioned MLST dataset, a generalized time-reversible (GTR) evolutionary model with inverse-gamma distribution was selected as the best-fit model.
To provide quantitative evidence from all studies and maximize statistical power for hypothesis testing, we performed meta-analyses using DerSimonian and Laird's random-effects model with inverse-variance (SE) weighting of individual study results (24) when data could be combined.
Site-specific estimates were then pooled using fixed-effect meta-analytic models with inverse variance weighting.
This was investigated across trials by using random effects meta-regression models with inverse variance weighting.
We used fixed- and random-effects models with inverse variance weighting to pool the risk estimates, when appropriate.
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