Suggests: mgcv, quantreg, VGAM, SuppDists, survival, parallel,

Title: Simulation-Based Sensitivity Analysis for Causal Mediation
Description: We implement the simulation-based sensitivity analysis for causal mediation studies using the methods
proposed by Qin and Yang (2020). It numerically and graphically evaluates the sensitivity of causal mediation analysis results
to the presence of unmeasured pretreatment confounding. The proposed method has primary advantages over existing methods.
First, using an unmeasured pretreatment confounder conditional associations with the treatment, mediator, and outcome as
sensitivity parameters, the method enables users to intuitively assess sensitivity in reference to prior knowledge about the
strength of a potential unmeasured pretreatment confounder. Second, the method accurately reflects the influence of unmeasured
pretreatment confounding on the efficiency of estimation of the causal effects. Third, the method can be implemented in
different causal mediation analysis approaches, including regression-based, simulation-based, and propensity score-based
methods. It is applicable to both randomized experiments and observational studies.

Description: Simulation-based sensitivity analysis for causal mediation studies. It numerically and graphically evaluates the sensitivity of causal mediation analysis results

Title: Data Package for Medical Datasets
Authors@R: c(person(given = "Peter", family = "Higgins",
email="higgi13425@yahoo.com", role = c("aut", "cre"),
Description: Provides access to well-documented medical datasets for teaching.
Featuring several from the Teaching of Statistics in the Health Sciences
website <https://www.causeweb.org/tshs/category/dataset/>, a few reconstructed datasets of historical significance in medical
research, some reformatted and extended from existing R packages,
Suggests: knitr, rmarkdown, markdown, learnr
Packaged: 2021-08-12 12:06:51 UTC; peterhiggins
Author: Peter Higgins [aut, cre] (<https://orcid.org/0000-0002-7111-0077>)