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We considered the following: impact of the risk of bias of individual trials; precision of the pooled estimate; inconsistency or heterogeneity (clinical, methodological and statistical); indirectness of evidence; impact of selective reporting and publication bias on effect estimate.
We could estimate inconsistency only for drop-outs because of adverse events, with some evidence of inconsistency (ratio of relative risks 0.54, 0.19 to 1.46, P=0.22 for inconsistency).
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The 95% interval of the estimated inconsistency by the RIBMTC method is much wider than by other methods.
Heterogeneity of treatment effects was assessed by graphical inspection of forest plots and formally using the Q statistic (at a p value ≤ 0.1) and I2 statistic for estimating inconsistency among study results.
The variance of the estimated inconsistency was calculated by: V a r ω BC = V a r d BC + V a r d BC Ind where Var(d BC ) and Var(d BC Ind ) are the variance of d BC and d BC Ind respectively.
The estimated inconsistencies by the different comparison methods are on average unbiased, but the 95% intervals are wide.
Random effects models were used to estimate the inconsistency.
The estimate of inconsistency is not applicable when the consistency Bayesian MTC model [ 8] is used.
We follow the same process for comparisons AC and BC and then we estimate the inconsistency factor.
The point estimate of inconsistency in Bayesian MTC was the average (mean value) of the simulated results.
For the archaeal dataset, we ran Impossible with the following command-line: Java Impossible F1 0.9 -a -g F2 -o F3, with F1 corresponding to the list of files of bipartitions, F2 being the description of groups to be tested by the rough estimate of inconsistency and to be listed in the pairwise diagram and F3, the name of the outfile at the pdf format.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com