McNemar's tests indicated four genera were also enriched in urine prior to adjustment for multiple sampling in this group: Pelomonas (p = 0.003, FDR = 16.70%), Propionibacterium (p = 0.004, FDR = 19.18%), Staphylococcus (p = 0.023, FDR = 60.69%), and Finegoldia (p = 0.023,FDR = 60.69%).
The association between respiratory allergy/hay fever and PM2.5 10 increased in the subset linked to multiple pollutants, both without and with adjustment for multiple pollutants, suggesting possible sample selection effects in this group.
This could be due to a low statistical power to detect an effect when one is present (type II error), which is in part explained by adjustment for multiple testing and small sample size.
The main strengths of our analysis include: a large sample size, adjustment for multiple potential confounders, sufficient information on exposure routes to evaluate nonsmoking nonoccupational cadmium exposure, and the use of an objective computer-based neuropsychological evaluation that was designed for epidemiologic applications [ 9].
While the use of a proportional odds model clearly delineates the importance of these predictors in terms of relative odds, this study was limited in its examination of the absolute magnitude of these discrepancies (i.e., the number of kilograms reported versus measured) after accounting for the study's complex sampling design and adjustment for multiple covariates.
Asterisks indicate significant difference, according to the Mann-Whitney test for independent samples with Bonferroni adjustment for multiple testing; *P < 0.05, **P < 0.01.
Before adjustment for multiple comparisons, lifetime psychotic symptoms significantly interacted with cortisol sampling year to predict logAUC (p < 0.0001).
Other partitions did not suggest early stopping after adjustment for multiple testing due to one influential outlier and our small sample size.
Before adjustment for multiple comparisons, there was also a significant interaction between SUD and cortisol sampling year predicting logAUC (p = 0.03) (Table 3).
Reporting of additional information such as adjustment for multiple testing (for example, multiple endpoints, multiple checks during interim monitoring) required for the sample size calculation was also documented.
A larger sample size would have increased the statistical power and would allow adjustment for multiple testing and covariates.
