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sensmediation (version 0.2.0)

Parametric Estimation and Sensitivity Analysis of Direct and Indirect Effects

Description

We implement functions to estimate and perform sensitivity analysis to unobserved confounding of direct and indirect effects introduced in Lindmark, de Luna and Eriksson (2018) . The estimation and sensitivity analysis are parametric, based on probit and/or linear regression models. Sensitivity analysis is implemented for unobserved confounding of the exposure-mediator, mediator-outcome and exposure-outcome relationships.

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Version

Install

install.packages('sensmediation')

Monthly Downloads

157

Version

0.2.0

License

GPL-2

Maintainer

Anita Lindmark

Last Published

November 14th, 2018

Functions in sensmediation (0.2.0)

Implementation of loglikelihood functions for ML estimation of regression parameters

Estimate additional natural direct and indirect effects based on an object from sensmediation

Implementations of the partial derivatives (gradients) of the expressions for the direct, indirect and total effects. Used to calculate standard errors (delta method).

Analytic Hessians of the loglikelihood functions for ML estimation of regression parameters

plot.effectsMed

Plot function for objects of class "effectsMed"

print.summaryeffectsMed

Print function for objects of class "summaryeffectsMed"

Estimate natural direct and indirect effects based on parametric regression models and perform sensitivity analysis

Function for estimation of natural direct and indirect effects and sensitivity analysis for unobserved mediator-outcome confounding

ML estimation of regression parameters for calculation of direct and indirect effects under unobserved confounding

Functions to calculate standard errors of the direct, indirect and total effects using the delta method.

summary.effectsMed

Summary function for objects of class "effectsMed"

Analytic gradients of the loglikelihood functions for ML estimation of regression parameters

Functions to calculate natural direct and indirect effects.

Functions for ML estimation of regression parameters for sensitivity analysis