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sensitivityCalibration  

A Calibrated Sensitivity Analysis for Matched Observational Studies
View on CRAN: Click here


Download and install sensitivityCalibration package within the R console
Install from CRAN:
install.packages("sensitivityCalibration")

Install from Github:
library("remotes")
install_github("cran/sensitivityCalibration")

Install by package version:
library("remotes")
install_version("sensitivityCalibration", "0.0.1")



Attach the package and use:
library("sensitivityCalibration")
Maintained by
Bo Zhang
[Scholar Profile | Author Map]
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First Published: 2018-12-18
Latest Update: 2018-12-18
Description:
Implements the calibrated sensitivity analysis approach for matched observational studies. Our sensitivity analysis framework views matched sets as drawn from a super-population. The unmeasured confounder is modeled as a random variable. We combine matching and model-based covariate-adjustment methods to estimate the treatment effect. The hypothesized unmeasured confounder enters the picture as a missing covariate. We adopt a state-of-art Expectation Maximization (EM) algorithm to handle this missing covariate problem in generalized linear models (GLMs). As our method also estimates the effect of each observed covariate on the outcome and treatment assignment, we are able to calibrate the unmeasured confounder to observed covariates. Zhang, B., Small, D. S. (2018). .
How to cite:
Bo Zhang (2018). sensitivityCalibration: A Calibrated Sensitivity Analysis for Matched Observational Studies. R package version 0.0.1, https://cran.r-project.org/web/packages/sensitivityCalibration. Accessed 07 Nov. 2024.
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