References | Mediator | Rationale | Approach | Results |
---|---|---|---|---|
Jackson et al. [29] | Medical events (binary) |
Algorithms with high positive-predictive values were used to identify medical events during follow up False negatives is a concern under some scenarios |
How results would change were examined given various scenarios of non-differential and differential misclassification Perfect specificity for observing the medical event, but varied the sensitivity from 0.25 to 0.75 separately for those who survived and for those who died was assumed Each scenario was assumed that mediator misclassification was non-differential with respect to antipsychotic type, covariates, and other mediators but some scenarios allowed for differential misclassification with respect to death. A hybrid approach was also used |
The proportion mediated was higher than the naïve estimators for some medical events and grew as sensitivity decreased from 0.75 to 0.25. The sensitivity among those who survived, rather than those who died, appeared to have more influence on these results It was suggested that 15 to 45 % of the mortality difference might be explained by some conditions given scenarios assumed compared to 9 % using naïve approach Authors suggested to address mediator misclassification when it is suspected, preferably through validation sub-studies or bias analyses |
Lu et al. [32] | Biomarkers (continuous) | Not reported |
The impact of measurement error in the mediators by calibrating the regression coefficients was assessed Assuming that 1-time measurements for each metabolic risk at baseline explain only 65 % of their true variability (i.e. 35 % measurement error) |
After correcting for a presumed 35 % measurement Error in each metabolic risk factor increased the overall the percentage of excess relative risk mediated from 47 % (33–63 %) to 69 % (52–87 %) for overweight, and from 52 % (38–68 %) to 73 % (58–88 %) for obesity |
Rao et al. [36] |
Smoking Chewing quid and/or tobacco Alcohol (binary) |
Dichotomization of mediator variable was done to simplify the analysis but the estimates from the analysis could be biased The sensitivity analysis for non-differential misclassification error of binary mediator was used |
The predictive value weighting estimators for outcome regression was used The sensitivity analysis was carried out without accounting for the clustering using the plausible sensitivity values ranging from 0.75 to 1.0 and specificity from 0.75 to 1.0 |
In the absence of exposure mediator interaction, the sensitivity analysis indicated a slight over estimation of the controlled direct effect The bias seemed to be larger when the sensitivity and specificity decreased |