Robust Inference for Mediated Effects in Partially Linear Models

07/01/2020
by   Oliver Hines, et al.
0

We consider mediated effects of an exposure, X on an outcome, Y, via a single mediator, M, under the assumption of no unmeasured confounding in the setting where models for the conditional expectation of the mediator and outcome are partially linear. In this setting the indirect effect is defined through a product of model coefficients, and the direct effect by a single model coefficient. We propose G-estimators for the direct and indirect effect and demonstrate consistent asymptotically normality for indirect effects when models for M, or X and Y are correctly specified, and for direct effects, when models for Y, or X and M are correct. This marks an improvement over previous `triple' robust methods, which do not assume partially linear mean models, but instead require correct specification of any pair of: the conditional expectation of Y and the conditional densities of M and X. Testing of the no-mediation hypothesis is inherently problematic due to the composite nature of the test (either X has no effect on M or M no effect on Y), leading to low power when both effect sizes are small. We use Generalized Methods of Moments (GMM) results to construct a new score testing framework, which includes as special cases the no-mediation and the no-direct-effect hypotheses. The proposed tests rely on a bias-reduction strategy for estimating parameters in nuisance confounder models. Simulations show that the GMM based tests perform better in terms of power and small sample performance compared with traditional tests in the partially linear setting, with drastic improvement under model misspecification. New methods are illustrated in a mediation analysis of data from the COPERS trial, a randomized trial on the effect of a non-pharmacological intervention of patients suffering from chronic pain. An accompanying R package implementing these methods can be found at github.com/ohines/plmed.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
11/09/2017

The generalized front-door criterion for estimation of indirect causal effects of a confounded treatment

The population intervention effect (PIE) of an exposure measures the exp...
research
06/02/2023

Alternative Measures of Direct and Indirect Effects

There are a number of measures of direct and indirect effects in the lit...
research
12/12/2020

Semiparametric causal mediation analysis under unmeasured mediator-outcome confounding

Although the exposure can be randomly assigned in studies of mediation e...
research
05/09/2022

Efficient and flexible estimation of natural mediation effects under intermediate confounding and monotonicity constraints

Natural direct and indirect effects are mediational estimands that decom...
research
07/15/2021

Optimal tests of the composite null hypothesis arising in mediation analysis

The indirect effect of an exposure on an outcome through an intermediate...
research
08/12/2022

Mediation Analyses for the Effect of Antibodies in Vaccination

We partition the total ratio effect (one minus the vaccine effect) from ...
research
12/09/2018

Bootstrapping F test for testing Random Effects in Linear Mixed Models

Recently Hui et al. (2018) use F tests for testing a subset of random ef...

Please sign up or login with your details

Forgot password? Click here to reset