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Therefore the analyses include relevant individual-level variables to control for this issue (see below).
Hence, we asked the participants to rate how safe they felt on the treadmill to control for this issue.
Unfortunately, no data were collected that allowed control for this issue, but it is unlikely that this would have a substantial influence on the results as all information and instructions were in Swedish, including all self-report measures.
The control for this issue in model 4 of Table 2 shows a markedly higher risk of disease at the time of cessation if a required time period since drinking cessation is not defined, which most likely is an effect of including individuals who stop drinking due to stomach problems.
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While we performed multivariate analysis controlling for this issue, this may still have influenced our results due to the small sample size.
There were significant difference in covariates that could be confounders or mediators of the effect of etiologic subgroup; however, we statistically controlled for this issue in our multivariable analysis.
Therefore, there is a clear need for improved control strategies for this issue including more effective diagnosis and control measures.
Integrated IRT models are designed to control for precisely this issue.
Fortunately, as Alice Eagly points out in her analysis, there are sophisticated statistical techniques that can control for these issues.
To control for these issues we performed a simulation to help us determine the best E-value threshold for a conopeptide hits in RNA-seq data.
However, comparison with blood samples from a normal control cohort may compensate for this issue, allowing threshold values for pathological marker levels in blood to be established.
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CEO of Professional Science Editing for Scientists @ prosciediting.com