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The individual study results (ie, log OR values and their corresponding SEs) were combined in a random effects meta-analysis model using the generic inverse variance outcome type in Review Manager.
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Time-to-event outcomes or generic inverse variance outcomes, such as survival time and time to cure, will be expressed as the log HR and 95% CI.
Multiple linear regression analyses were carried out to identify the variables that made a significant unique contribution to explaining the variance in outcome, using firstly global treatment outcome and then satisfaction with treatment as the dependent variable to be predicted.
However, that study had major shortcomings from severe lack of statistical power due to the large variance in outcome incidence and wide standard deviation and the lower-than-expected incidence of the outcome measures under investigation.
Several patient factors have been identified as accounting for some of the variance in outcome after knee replacement; however which patient factors are important, and how much they affect outcome remains uncertain.
Significant variance in outcome still remains, indicating that additional methods may be necessary to quantify brain damage after SAH.
Our approach uses pathway signatures to predict variance in outcome, which is taken as the measure of drug effectiveness.
We found that 25% of the variance in outcome could be attributed to between-surgeon variance.
This implies a loss of variance in outcome measures so that associations in the regression analysis might have been underestimated.
The Parkin score and maternal height picked up some of the variance in outcome variables as well.
A prediction model in itself does not indicate how much variance in outcome is explained by the included variables.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com