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For example: if the difference in mean age between two study populations is lager than 5 years, age is considered an important factor according to the results of the AQUILA.
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There was no significant difference between ratings of seriousness and disease-like status between the two study populations.
The largest difference between the two study populations was found for the disagreement between the self- and proxy-reports on the "self-esteem" dimension (β = 5.1).
Despite the different ranges of coffee consumption between the two study populations, we also did not observe a significant association between coffee intake and breast cancer mortality.
Gravidity, gestational age and birth weight differed between the two study populations in cases and controls.
This analysis is limited by the slightly different study populations as evidenced by demographic differences found between the two study populations.
Minor genetic heterogeneity can also offer some alternative explanations, because the frequencies of several HLA-B and HLA-C alleles differed between the two study populations (Table 2).
This may reflect the difference in clinical baseline parameters between the two study populations occurring as a result of lower mean age (35 vs. 44 yrs) and a lower EDSS at study entry (1.7 vs. 2.8) in our patient group.
For this reason we consider unlikely that demographic differences between the two study populations can explain the observed differences in levels of poultry handling or poultry mortality reporting practices.
While this would affect estimates of SRHS within NHANES-III, it is doubtful that the misclassification of radiographic severity would account for the large discrepancy between the two study populations.
The distribution of other factors is reasonably similar between the two study populations.
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CEO of Professional Science Editing for Scientists @ prosciediting.com