OR: Causal odds ratio of a binary/continuous treatment variable

In GJRM: Generalised Joint Regression Modelling

Causal odds ratio of a binary/continuous treatment variable

Description

OR can be used to calculate the causal odds ratio of a binary/continuous treatment variable, with corresponding interval obtained using posterior simulation.

Usage

OR(x, trt, int.var = NULL, joint = TRUE, n.sim = 100, prob.lev = 0.05, 
   length.out = NULL)

Arguments

x	A fitted gjrm object.
trt	Name of the treatment variable.
int.var	A vector made up of the name of the variable interacted with nm.end, and a value for it.
joint	If FALSE then the effect is obtained from the univariate model which neglects the presence of unobserved confounders. When TRUE, the effect is obtained from the simultaneous model which accounts for observed and unobserved confounders.
n.sim	Number of simulated coefficient vectors from the posterior distribution of the estimated model parameters. This is used when delta = FALSE. It may be increased if more precision is required.
prob.lev	Overall probability of the left and right tails of the OR distribution used for interval calculations.
length.out	Ddesired length of the sequence to be used when calculating the effect that a continuous treatment has on a binary outcome.

Details

OR calculates the causal odds ratio for a binary/continuous Gaussian treatment. Posterior simulation is used to obtain a confidence/credible interval.

Value

prob.lev	Probability level used.
sim.OR	It returns a vector containing simulated values of the average OR. This is used to calculate intervals.
Ratios	For the case of continuous endogenous treatment and binary outcome, it returns a matrix made up of three columns containing the odds ratios for each incremental value in the endogenous variable and respective intervals.

Author(s)

Maintainer: Giampiero Marra [e-mail]

See Also

GJRM-package, gjrm

GJRM documentation built on Oct. 25, 2024, 5:07 p.m.