| Title: | Mixed-Scale Treatment Effect Robust Identification (MR MiSTERI) and Estimation |
|---|---|
| Description: | This package performs robust Mendelian randomization to estimate the effect of treatment on the treated with possibly invalid IVs. |
| Authors: | Zhonghua Liu, Ting Ye, Baoluo Sun and Eric Tchetgen Tchetgen |
| Maintainer: | Zhonghua Liu <[email protected]> |
| License: | MIT |
| Version: | 0.1.0 |
| Built: | 2024-10-31 16:33:10 UTC |
| Source: | https://github.com/remlapmot/MRMiSTERI |
This function estimates the causal effect of treatment on the treated (ETT) for a continous outcome with Gaussian error terms.
misterigauss(Z = Z, A = A, Y = Y)misterigauss(Z = Z, A = A, Y = Y)
Z |
an IV scalar variable |
A |
the exposure variable |
Y |
the continuous outcome variable |
a list object that contains causal effect estimates and standard errors.
https://www.medrxiv.org/content/10.1101/2020.09.29.20204420v3
This function estimates the causal effect of treatment on the treated (ETT) for a continous outcome with error terms that follow Gaussian mixture distributions.
misterigaussmix(Z, A, Y, maxiter = 100, tol = 0.001)misterigaussmix(Z, A, Y, maxiter = 100, tol = 0.001)
Z |
an IV scalar variable |
A |
the exposure variable |
Y |
the continuous outcome variable |
a list object that contains causal effect estimates and standard errors.
https://www.medrxiv.org/content/10.1101/2020.09.29.20204420v3 #' @import alabama
misterimawii combines many weak invalid IVs to reduce weak IV bias.
misterimawii(Z, A, Y)misterimawii(Z, A, Y)
Z |
an IV matrix with columns representing IVs |
A |
the exposure variable |
Y |
the continuous outcome variable |
a list object that contains causal effect estimates and standard errors.
https://www.medrxiv.org/content/10.1101/2020.09.29.20204420v3