This package performs a dual-parameter sensitivity analysis of treatment effect to unmeasured confounding in observational studies with either survival or competing risks outcomes.
12/27/2022 Version 1.1.0
Motivated by Cinelli, C., & Hazlett, C. (2020), we introduce partial
In the outcome model, suppose the full model
In the treatment model, according to Cox, D. R., & Snell, E. J. (1989), the full model
The returned partial
04/29/2020 Version 0.1.0
Performs a dual-parameter sensitivity analysis of treatment effect to unmeasured confounding in observational studies with either survival or competing risks outcomes as described in Huang, R., Xu, R., & Dulai, P. S. (2020).
if(!require(devtools))install.packages("devtools")
devtools::install_github("Rong0707/survSens")
For survival outcomes,
# Load the dataset included in the package.
data(survdata)
# Stochastic EM with regression
tau.res = survSensitivity(survdata$t, survdata$d, survdata$Z, survdata$X, "stoEM_reg", B = 5)
# EM with regression
tau.res = survSensitivity(survdata$t, survdata$d, survdata$Z, survdata$X, "EM_reg", Bem = 50)
# Contour plot with coefficients as axes.
plotsens(tau.res, coeff0 = 1.131)
# Contour plot with partial R-squared as axes.
plotsens(tau.res, coeff0 = 1.131, TRUE)
For competing risks outcomes,
# Load the dataset included in the package
data(comprdata)
# Stochastic EM with regression
tau.res = comprSensitivity(comprdata$t, comprdata$d, comprdata$Z, comprdata$X, "stoEM_reg", B = 5)
# EM with regression
tau.res = comprSensitivity(comprdata$t, comprdata$d, comprdata$Z, comprdata$X, "EM_reg", Bem = 50)
# Contour plot with coefficients as axes.
plotsens(tau.res$tau1, coeff0 = 1.244)
# Contour plot with partial R-squared as axes.
plotsens(tau.res$tau1, coeff0 = 1.244, TRUE)
Output consists of dataframe(s) for estimated treatment effect(s), as well as a contour plot for visualization.
- Cinelli, C., & Hazlett, C. (2020). Making sense of sensitivity: Extending omitted variable bias. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 82(1), 39-67.
- Huang, R., Xu, R., & Dulai, P. S. (2020). Sensitivity analysis of treatment effect to unmeasured confounding in observational studies with survival and competing risks outcomes. Statistics in Medicine, 39(24), 3397-3411.
- O'Quigley, J., Xu, R., & Stare, J. (2005). Explained randomness in proportional hazards models. Statistics in medicine, 24(3), 479-489.
- Cox, D. R., & Snell, E. J. (1989). The analysis of binary data, 2nd ed. London: Chapman and Hall.