Year: 2012
Type: article
Abstract: Mediators are intermediate variables in thecausal pathway between an exposure and an outcome. Mediation
analysis investigates the extent to which exposure effects occur
through these variables, thus revealing causal mechanisms. One
interesting question in causal inference area is mediation analysis
for non-linear models. In the first part of this
dissertation, we consider the estimation of mediation effects in
zero-inflated (ZI) models intended to accommodate ‘extra’ zeros in
count data. Focusing on the ZI negative binomial (ZINB) models, we
provide a mediation formula approach to estimate the (overall)
mediation effect in the standard two-stage mediation framework
under the key sequential ignorability assumption. We also consider
a novel decomposition of the overall mediation effect for the ZI
context using a three-stage mediation model. Simulation study
results demonstrate low bias of mediation effect estimators and
close-to-nominal coverage probability (CP) of confidence intervals.
The method is applied to a retrospective cohort study of dental
caries in very low birth weight adolescents. For overall mediation
effect estimation, sensitivity analysis was conducted to quantify
the degree to which key assumption must be violated to reverse the
original conclusion. The second question we focus
on is the mediation analysis for a dichotomous outcome in
multiple-mediator models. We formulate a joint model
(probit-normal) using continuous latent variables for any binary
mediators to account for correlations among multiple mediators. A
mediation formula approach is proposed to estimate the total
mediation effect and decomposed mediation effects based on this
parametric model. We conduct a simulation study that demonstrates
low bias of mediation effect estimators for two-mediator models
with various combinations of mediator types. The results also show
that the power to detect a non-zero total mediation effect
increases as the correlation coefficient between two mediators
increases, while power for individual mediation effects reaches a
maximum when the mediators are uncorrelated. We illustrate our
approach by applying it to a retrospective cohort study of dental
caries in adolescents with low and high socioeconomic status.
Sensitivity analysis is performed to assess the robustness of
conclusions regarding mediation effects when the assumption of no
unmeasured mediator-outcome confounders is
violated
Author: Wei Wang
Institution: Case Western Reserve University
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2286593271