In our paper (1), we considered the extent and patterns of bias in estimates of exposure-outcome associations that can result from residual or unmeasured confounding, when there is no true association between the exposure and the outcome. We conducted simulations with two or four con-founders. When the confounders are uncorrelated, bias in the exposure effect estimate increases as the amount of residual and unmeasured confounding increases. However, patterns are more complex for correlated confounders: It is possible for the bias to increase when confounder measurement error decreases or when additional confounders are controlled for
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