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However, with a grouped exposure it can be misleading to interpret ratios for individual groups as independent t-statistics.
Thus, we interpret ratios of test and control gene counts from the nCounter as true measures of the relative expression of these genes in our samples.
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We interpreted ratios as absence of hypermethylation (0.00 0.24), mild hypermethylation (0.25 0.49), moderate hypermethylation (0.50 0.74), and extensive hypermethylation (≥0.75), as previously described [18].
FAQ: How do I interpret odds ratios in logistic regression?
In this paper, the proportional odds model is used to interpret odds ratios for cumulative probabilities.
Overall, it is expected that the study design is robust enough to interpret hazard ratios of 1.5 or higher.
We interpret rate ratios less than 1 as indicating the overall rate of event in the intervention group is 100 [1 - exp ]% lower than in the control.
We interpret rate ratios less than 1 as indicating that among those who experienced j - 1 events, the intervention reduces the rate of the jth event by 100[1 - exp (β ^ j ) ]% compared to the control.
We interpret rate ratios less than 1 as indicating the overall rate of event, that is the rate of any event, in the intervention group is 100 [1 - exp ]% lower than in the control.
We interpret rate ratios less than 1 as indicating the transition rate from 0 to j events in the intervention group is 100 [1 - exp (β ^ j ) ]% lower than in the control.
Exponentiating the parameter estimates allows to interpret odds ratios as relative risks if the number of events is relatively small in comparison to the number of exposures (Woodward 1999), a condition which is fulfilled for our analysis.
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