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In the later models, all estimates of association were adjusted for the diabetes risk factors minus the estimated total lipid concentration.
Therefore, any estimates of association will be conservative.
In this review, we present an example of four potentially important biases (lead time bias, survivor bias, immortal time bias, and index event bias) that can result in inaccurate estimates of association between risk factors or treatments and outcomes.
The beta coefficients were then combined across studies using random effects meta-analysis, and forest plots were used to display the study-specific and combined estimates of association and 95% confidence intervals.
Despite this, we found near or significant associations with responses to the three questions about heart disease in NHANES 2005/06, and highly significant p-values for the pooled estimates of association.
Before routinely collapsing information across strata we considered it important to obtain estimates of association for each stratum using x2 Mantel-Haenszel statistics to make sure that they could be meaningfully pooled [46].
Summary estimates of association were obtained using random effects models.
Therefore, adjusting for these factors may have produced conservative estimates of association, arguably an overadjustment.
Model 1 showed estimates of association between each indicator of SES and MetS without the adjustments.
This may impact the prevalence, but estimates of association are more robust.
Adjustment for more finely divided age categories made no difference in the estimates of association.
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