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Non-parametric tests were run for all the accuracy scores because our variables did not show normality or homogeneity.
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We used the Wilcoxon test because the variables did not follow a normal distribution.
Current smoking status at baseline visit and HAART ever-use were included because these variables did not change over time.
Because these variables did not provide additional insight in the results compared with the respective unlagged variables, they were not considered in any further analyses.
However, because some variables did not meet the assumption of normalcy, logistic regression analyses were also run to confirm the findings.
However, because these variables did not contribute significantly to the prediction models, they were omitted from the final models, as described above.
A ± 1 SD range was chosen because the variables did not always follow a normal distribution, giving poor sense to a ± 2 SD range representation.
For hours worked in the public and in the private sector, we used GLMs because these variables did not present an excess number of zeros.
However, because these variables did not alter the relationship between the lead biomarkers and tooth loss and were not significant predictors of tooth loss, we excluded them from our final analyses.
Because these variables did not alter HRs significantly after adjusting for other covariates (statistical significance was set at P < 0.05) and the full model adjusted for all covariates made no substantial difference to the results compared to the reduced model.
Because most variables did not always fulfill the normality hypothesis, differences between groups were analyzed using the Mann-Whitney U-test for nonparametric data and analysis of variance followed by a Wilcoxon signed-rank test were used for within-group analyses.
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CEO of Professional Science Editing for Scientists @ prosciediting.com