Causal Inference for Mediation Effects

of “Causal Inference for Mediation Effects,” by Jing Zhang, Ph.D., Brown University, May 2012 This thesis work is motivated by the specific challenges in analyzing data from behavioral trials. Specifically, SRIDE study designed to increase physical activity. Typically, the objective is to understand how the effect of an intervention operates on a primary outcome through and around potential mediating variables. The overall effect of a treatment can be broken down into its direct and indirect effects. The indirect effect is the effect of intervention on the outcome passing through the mediator, and the direct effect is the effect flowing around the mediator. The most widely used approach to mediation analysis is the Baron-Kenny method. However, it requires that subjects are randomized to baseline intervention and, following randomization, to the mediator levels within each intervention group. Other methods assume each individual can have potential outcomes at each level of the mediator, and they measure controlled effects. In behavior trials, when the mediator reflects inherent characteristics of an individual, such as motivation to excerise, models capturing controlled effect may not be appropriate, because the mediator cannot be externally manipulated. Here, we propose methodology to address this shortcoming, and estimate natural causal effects. The natural direct effect measures the intervention effect when the mediator for each subject is fixed at its potential level under no intervention, while the natural indirect effect measures the effect of intervention going through the mediator by contrasting the mediator value under intervention to its value under no intervention. Although these methods are motivated by behavioral data, they are applicable in other clinical trial settings as well. The first part of this thesis proposed three methods to estimate natural direct and indirect effects in one mediator context of randomized trials: inverse probability weighting (IPW), regression imputation (REG) and augmented inverse probability weighting (AIPW). We use baseline covariates to impute the unobserved potential mediator, and use a sensitivity parameter to capture association between two potential mediators. The unobserved potential outcome is treated as a missing value, whose expectation can be consistently estimated under some assumptions. We study the properties of our methods in simulation studies and illustrate using an analysis of a recent intervention trial designed to increase physical activity. The second part of this thesis develop a model to estimate natural causal mediation effects when there are several potential mediators in randomized trials, and decompose the total intervention effect into natural direct and specific indirect effects. Our model identifies the joint distribution of potential mediating variable given baseline covariates and targeted restrictions to the correlation structure of the potential mediators. Unobserved potential mediators and associated potential outcomes can therefore be imputed under the model, and causal contrasts of interest can be computed in a straightforward manner. We illustrate our methods using an analysis of a recent intervention trial designed to increase physical activity. The third part of this thesis examine causal mediation analysis in the survival data of observational study. A matched nutrition dataset is obtained from USAID-AMPATH program using optimal matching, and we propose a method to identify natural causal effects in survival probability odds ratio scale. Unobserved potential outcomes of each individual in the treatment group when the mediator is fixed its value under the control, can be imputed based on baseline covariates and mediator values of matched controls. Causal contrast of interest can be inferred based on conditional logistic regression accounting for the matching property.