261 research outputs found
Natural direct and indirect effects on the exposed : effect decomposition under weaker assumptions
We define natural direct and indirect effects on the exposed. We show that these allow for effect decomposition under weaker identification conditions than population natural direct and indirect effects. When no confounders of the mediator-outcome association are affected by the exposure, identification is possible under essentially the same conditions as for controlled direct effects. Otherwise, identification is still possible with additional knowledge on a nonidentifiable selection-bias function which measures the dependence of the mediator effect on the observed exposure within confounder levels, and which evaluates to zero in a large class of realistic data-generating mechanisms. We argue that natural direct and indirect effects on the exposed are of intrinsic interest in various applications. We moreover show that they coincide with the corresponding population natural direct and indirect effects when the exposure is randomly assigned. In such settings, our results are thus also of relevance for assessing population natural direct and indirect effects in the presence of exposure-induced mediator-outcome confounding, which existing methodology has not been able to address
Sharp sensitivity bounds for mediation under unmeasured mediator-outcome confounding
It is often of interest to decompose a total effect of an exposure into the
component that acts on the outcome through some mediator and the component that
acts independently through other pathways. Said another way, we are interested
in the direct and indirect effects of the exposure on the outcome. Even if the
exposure is randomly assigned, it is often infeasible to randomize the
mediator, leaving the mediator-outcome confounding not fully controlled. We
develop a sensitivity analysis technique that can bound the direct and indirect
effects without parametric assumptions about the unmeasured mediator-outcome
confounding
Sensitivity Analysis for Unmeasured Confounding in Meta-Analyses
Random-effects meta-analyses of observational studies can produce biased
estimates if the synthesized studies are subject to unmeasured confounding. We
propose sensitivity analyses quantifying the extent to which unmeasured
confounding of specified magnitude could reduce to below a certain threshold
the proportion of true effect sizes that are scientifically meaningful. We also
develop converse methods to estimate the strength of confounding capable of
reducing the proportion of scientifically meaningful true effects to below a
chosen threshold. These methods apply when a "bias factor" is assumed to be
normally distributed across studies or is assessed across a range of fixed
values. Our estimators are derived using recently proposed sharp bounds on
confounding bias within a single study that do not make assumptions regarding
the unmeasured confounders themselves or the functional form of their
relationships to the exposure and outcome of interest. We provide an R package,
ConfoundedMeta, and a freely available online graphical user interface that
compute point estimates and inference and produce plots for conducting such
sensitivity analyses. These methods facilitate principled use of random-effects
meta-analyses of observational studies to assess the strength of causal
evidence for a hypothesis
- …