Many methods have been proposed to solve the age-period-cohort (APC) linear identification problem, but most are not theoretically informed and may lead to biased estimators of APC effects. One exception is the mechanism-based approach recently proposed and based on Pearl’s front door criterion; it ensures consistent APC effect estimators in the presence of a complete set of intermediate variables between one of age, period, cohort and the outcome of interest, as long as the assumed parametric models for all the relevant causal pathways are correct. Through a simulation study mimicking APC data on cardiovascular mortality, we assess the performance of the mechanism-based approach under realistic conditions, namely when 1) the set of available intermediate variables is incomplete; 2) intermediate variables are affected by two or more of the APC variables, but this feature is not acknowledged in the analysis 3) unaccounted confounding is present between intermediate variables and the outcome. Furthermore, we show how the mechanism-based approach can be extended beyond the originally proposed linear and probit regression models to incorporate all generalized linear models, as well as non-linearities in the predictors using Monte Carlo simulation. We find that the mechanism-based approach (extended or not) is only slightly affected by bias when the departures from the assumptions are small.
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