Alternative Monotonicity Assumptions for Improving Bounds on Natural Direct Effects

Chiba, Yasutaka; Taguri, Masataka

doi:10.1515/ijb-2012-0022

Cited by 6 publications

(9 citation statements)

References 18 publications

(40 reference statements)

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“…In the single mediator setting, there are many works that propose a sensitivity analysis method, 15,17,23,35,36 including the derivation of bounds for the natural direct and indirect effects. 4,5,11,17,37 Investigation into how such methods can be adapted to this setting is another important area for future research.…”

Section: Discussionmentioning

confidence: 99%

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Causal mediation analysis with multiple causally non-ordered mediators

Taguri

Featherstone

Cheng

2015

Stat Methods Med Res

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In many health studies, researchers are interested in estimating the treatment effects on the outcome around and through an intermediate variable. Such causal mediation analyses aim to understand the mechanisms that explain the treatment effect. Although multiple mediators are often involved in real studies, most of the literature considered mediation analyses with one mediator at a time. In this article, we consider mediation analyses when there are causally non-ordered multiple mediators. Even if the mediators do not affect each other, the sum of two indirect effects through the two mediators considered separately may diverge from the joint natural indirect effect when there are additive interactions between the effects of the two mediators on the outcome. Therefore, we derive an equation for the joint natural indirect effect based on the individual mediation effects and their interactive effect, which helps us understand how the mediation effect works through the two mediators and relative contributions of the mediators and their interaction. We also discuss an extension for three mediators. The proposed method is illustrated using data from a randomized trial on the prevention of dental caries.

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Section: Discussionmentioning

confidence: 99%

“…There is growing literature on evaluating natural direct and indirect effects. [3][4][5][6][7][8][9][10][11] Although a treatment often affects the outcome through multiple mediators in real studies, most of the literature considered a mediation analysis with a single mediator only.…”

Section: Introductionmentioning

confidence: 99%

Causal mediation analysis with multiple causally non-ordered mediators

Taguri

Featherstone

Cheng

2015

Stat Methods Med Res

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“…In such situations, unfortunately, the methods presented here cannot be applied. Nevertheless, for unconditional infectiousness and contagion effects, we can still apply the methods developed in the context of the mediation analysis approach [30][31][32][33], and for the conditional infectiousness effect, we can also apply the methods developed in the context of t ation approach [25][26][27][28]. However, these methods have a weakness in that they are more complex than those in this paper.…”

Section: Discussionmentioning

confidence: 99%

A Simple Method of Measuring Vaccine Effects on Infectiousness and Contagion

Chiba¹

2013

OJS

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The vaccination of one person may prevent another from becoming infected, either because the vaccine may prevent the first person from acquiring the infection and thereby reduce the probability of transmission to the second, or because, if the first person is infected, the vaccine may impair the ability of the infectious agent to initiate new infections. The former mechanism is referred as a contagion effect and the latter is referred as an infectiousness effect. By applying a principal stratification approach, the conditional infectiousness effect has been defined, but the contagion effect is not defined using this approach. Recently, new definitions of unconditional infectiousness and contagion effects were provided by applying a mediation analysis approach. In addition, a simple relationship between conditional and unconditional infectiousness effects was found under a number of assumptions. These two infectiousness effects can be assessed by very simple estimation and sensitivity analysis methods under the assumptions. Nevertheless, such simple methods to assess the contagion effect have not been discussed. In this paper, we review the methods of assessing infectiousness effects, and apply them to the inference of the contagion effect. The methods provided here are illustrated with hypothetical vaccine trial data.

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“…Thus, here, we present bounds for the bias term under sequential ignorability with or without M ‐monotonicity, in the binary outcome case. Several authors previously discussed the nonparametric bounds of NDEs (or NIEs) under A1 . Recently, Tchetgen Tchetgen and Phiri derived bounds for NDEs under assumptions A1 and A2.…”

Section: Bias Formula and Sensitivity Analysismentioning

confidence: 99%

“…We obtain bounds on NIE(0) from those for NIE(0| c ) by marginalizing over the empirical distribution of C . We also apply the nonparametric bounds presented in Chiba and Taguri under A1 as a reference. The results are summarized in Table .…”

Section: Applicationmentioning

confidence: 99%

A principal stratification approach for evaluating natural direct and indirect effects in the presence of treatment‐induced intermediate confounding

Taguri

Chiba

2014

Statistics in Medicine

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Recently, several authors have shown that natural direct and indirect effects (NDEs and NIEs) can be identified under the sequential ignorability assumptions, as long as there is no mediator-outcome confounder that is affected by the treatment. However, if such a confounder exists, NDEs and NIEs will generally not be identified without making additional identifying assumptions. In this article, we propose novel identification assumptions and estimators for evaluating NDEs and NIEs under the usual sequential ignorability assumptions, using the principal stratification framework. It is assumed that the treatment and the mediator are dichotomous. We must impose strong assumptions for identification. However, even if these assumptions were violated, the bias of our estimator would be small under typical conditions, which can be easily evaluated from the observed data. This conjecture is confirmed for binary outcomes by deriving the bounds of the bias terms. In addition, the advantage of our estimator is illustrated through a simulation study. We also propose a method of sensitivity analysis that examines what happens when our assumptions are violated. We apply the proposed method to data from the National Center for Health Statistics.

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Alternative Monotonicity Assumptions for Improving Bounds on Natural Direct Effects

Cited by 6 publications

References 18 publications

Causal mediation analysis with multiple causally non-ordered mediators

Causal mediation analysis with multiple causally non-ordered mediators

A Simple Method of Measuring Vaccine Effects on Infectiousness and Contagion

A principal stratification approach for evaluating natural direct and indirect effects in the presence of treatment‐induced intermediate confounding

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