A further critique of the analytic strategy of adjusting for covariates to identify biologic mediation

Abstract

BackgroundEpidemiologic research is often devoted to etiologic investigation, and so techniques that may facilitate mechanistic inferences are attractive. Some of these techniques rely on rigid and/or unrealistic assumptions, making the biologic inferences tenuous. The methodology investigated here is effect decomposition: the contrast between effect measures estimated with and without adjustment for one or more variables hypothesized to lie on the pathway through which the exposure exerts its effect. This contrast is typically used to distinguish the exposure's indirect effect, through the specified intermediate variables, from its direct effect, transmitted via pathways that do not involve the specified intermediates.MethodsWe apply a causal framework based on latent potential response types to describe the limitations inherent in effect decomposition analysis. For simplicity, we assume three measured binary variables with monotonic effects and randomized exposure, and use difference contrasts as measures of causal effect. Previous authors showed that confounding between intermediate and the outcome threatens the validity of the decomposition strategy, even if exposure is randomized. We define exchangeability conditions for absence of confounding of causal effects of exposure and intermediate, and generate two example populations in which the no-confounding conditions are satisfied. In one population we impose an additional prohibition against unit-level interaction (synergism). We evaluate the performance of the decomposition strategy against true values of the causal effects, as defined by the proportions of latent potential response types in the two populations.ResultsWe demonstrate that even when there is no confounding, partition of the total effect into direct and indirect effects is not reliably valid. Decomposition is valid only with the additional restriction that the population contain no units in which exposure and intermediate interact to cause the outcome. This restriction implies homogeneity of causal effects across strata of the intermediate.ConclusionsReliable effect decomposition requires not only absence of confounding, but also absence of unit-level interaction and use of linear contrasts as measures of causal effect. Epidemiologists should be wary of etiologic inference based on adjusting for intermediates, especially when using ratio effect measures or when absence of interacting potential response types cannot be confidently asserted.

Subclinical coronary atherosclerosis and neighbourhood deprivation in an urban region

“…In fact, an attenuation of the contextual effect on CAC was observed, in particular when comparing the difference between participants from the highest and the lowest neighbourhood deprivation level. However, these results should be interpreted with caution, because (a) mediation cannot be adequately analysed in a cross-sectional study design and (b) adjustment for intermediate variables is only appropriate under specific assumptions which might not have been fulfilled in this study [42].…”

A sufficient cause based approach to the assessment of mediation

“…Given perfect additivity, the PIE and TIE also correspond to indirect effect definitions based on other analytic approaches [15,29]. When there are departures from additivity, these equalities will generally not hold [15,24].…”

“…More complex models must be used if effects are both protective and harmful. The assumption of monotonicity is commonly made when developing response type frameworks for mediation [15,23,24].…”

Mediation and mechanism

“…Some of the effects of an exposure X on outcome Y may be thought to be mediated by some intermediate M and it will sometimes be of interest to determine the proportion of the effect of X on Y that is mediated by M. Such questions arise with considerable regularity within the social sciences; though, as noted by Hafeman, until recently these questions have been posed with only limited frequency by epidemiologists. The structural equation methods typically employed in the social sciences to address questions of mediation [2][3][4][5][6] have been subject to a number of limitations and criticisms concerning the inapplicability of the approach in the presence of interactions and non-linearities [7][8][9]. Recent literature in causal inference has sought to address some of these limitations and to supply definitions of direct and indirect effects that allow for effect decomposition even in settings with interactions and non-linearities [7][8][9][10][11]; this causal inference literature also clarified the no-unmeasured confounding assumptions necessary to identify direct and indirect effects and in particular noted the need to control for confounders of the relationships between the mediator and the outcome when conditioning on the mediator in the analysis.…”