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Using multiple outcome methods, such as blind clinician raters, would strengthen future research.
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The inclusion of three assessment points, multiple outcome measures, and robust statistical methods affords greater confidence in the findings.
We are investigating extensions of the node-split method to multiple outcome networks and investigate the effect of jointly synthesising evidence across multiple endpoints on evidence consistency in a simulation study.
Such testing involves comparing treatments using multiple outcome measures (MOMs) with univariate statistical methods.
We have presented methods for simultaneous comparison of multiple treatments across multiple outcome measures while preserving the internal randomisation of individual studies.
Our aim in this paper was to present methods for simultaneous comparison of multiple treatments across multiple outcome measures while preserving the internal randomisation of individual studies.
The use of multiple outcome measures in those trials reviewed reflects the lack of consensus on which method of assessing motor function is the most useful.
Different statistical methods have been proposed to adjust confidence intervals and P-values when multiple outcome comparisons are used [ 12].
In this study, we have presented the CCA method for exploring registered data using a proposed model for predictors and multiple outcome variables.
Outcome methods have also varied.
One publication could provide multiple outcome comparisons.
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