2,840 research outputs found
Deep determinism and the assessment of mechanistic interaction between categorical and continuous variables
Our aim is to detect mechanistic interaction between the effects of two
causal factors on a binary response, as an aid to identifying situations where
the effects are mediated by a common mechanism. We propose a formalization of
mechanistic interaction which acknowledges asymmetries of the kind "factor A
interferes with factor B, but not viceversa". A class of tests for mechanistic
interaction is proposed, which works on discrete or continuous causal
variables, in any combination. Conditions under which these tests can be
applied under a generic regime of data collection, be it interventional or
observational, are discussed in terms of conditional independence assumptions
within the framework of Augmented Directed Graphs. The scientific relevance of
the method and the practicality of the graphical framework are illustrated with
the aid of two studies in coronary artery disease. Our analysis relies on the
"deep determinism" assumption that there exists some relevant set V - possibly
unobserved - of "context variables", such that the response Y is a
deterministic function of the values of V and of the causal factors of
interest. Caveats regarding this assumption in real studies are discussed.Comment: 20 pages including the four figures, plus two tables. Submitted to
"Biostatistics" on November 24, 201
Integrated multiple mediation analysis: A robustness–specificity trade-off in causal structure
Recent methodological developments in causal mediation analysis have addressed several issues regarding multiple mediators. However, these developed methods differ in their definitions of causal parameters, assumptions for identification, and interpretations of causal effects, making it unclear which method ought to be selected when investigating a given causal effect. Thus, in this study, we construct an integrated framework, which unifies all existing methodologies, as a standard for mediation analysis with multiple mediators. To clarify the relationship between existing methods, we propose four strategies for effect decomposition: two-way, partially forward, partially backward, and complete decompositions. This study reveals how the direct and indirect effects of each strategy are explicitly and correctly interpreted as path-specific effects under different causal mediation structures. In the integrated framework, we further verify the utility of the interventional analogues of direct and indirect effects, especially when natural direct and indirect effects cannot be identified or when cross-world exchangeability is invalid. Consequently, this study yields a robustness–specificity trade-off in the choice of strategies. Inverse probability weighting is considered for estimation. The four strategies are further applied to a simulation study for performance evaluation and for analyzing the Risk Evaluation of Viral Load Elevation and Associated Liver Disease/Cancer data set from Taiwan to investigate the causal effect of hepatitis C virus infection on mortality
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
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