survSensitivity {survSens}

Sensitivity analysis of treatment effect to unmeasured confounding with survival outcomes.

Description

survSensitivity performs a dual-parameter sensitivity analysis of treatment effect to unmeasured confounding in observational studies with survival outcomes.

Usage

survSensitivity(t, d, Z, X, method, zetaT = seq(-2,2,by=0.5),
zetaZ = seq(-2,2,by=0.5), theta = 0.5, B = 50, Bem = 200)

Arguments

t

survival outcomes.

d

indicator of occurrence of event, with d == 0 denotes right censoring.

Z

indicator of treatment.

X

pre-treatment covariates that will be included in the model as measured confounders.

method

needs to be one of "stoEM_reg", "stoEM_IPW", and "EM_reg".

zetaT

range of coefficient of U in the response model.

zetaZ

range of coefficient of U in the treatment model.

theta

marginal probability of U=1.

B

iteration in the stochastic EM algorithm.

Bem

iteration used to estimate the variance-covariance matrix in the EM algorithm.

Details

This function performs a dual-parameter sensitivity analysis of treatment effect to unmeasured confounding by either drawing simulated potential confounders U from the conditional distribution of U given observed response, treatment and covariates or the Expectation-Maximization algorithm. We assume U is following Bernoulli(\pi) (default 0.5). Given Z, X and U, the hazard rate is modeled using the Cox proportional hazards (PH) regression:

\lambda (t | Z, X, U) = \lambda_{0} (t) exp(\tau Z + X ' \beta + \zeta U).

Given X and U, Z follows a generalized linear model:

P( Z=1 | X,U ) = \Phi(X' \beta_z + \zeta_z U).

Value

tau

a data.frame with zetaz, zetat, tau1, tau1.se and t statistic.

Author(s)

Rong Huang

References

Huang, R., Xu, R., & Dulai, P. S. (2019). Sensitivity Analysis of Treatment Effect to Unmeasured Confounding in Observational Studies with Survival and Competing Risks Outcomes. arXiv preprint arXiv:1908.01444.

Examples

#load the dataset included in the package.
data(survdata)
#stochastic EM with regression
tau.sto = survSensitivity(survdata$t, survdata$d, survdata$Z, survdata$X,
"stoEM_reg", zetaT = 0.5, zetaZ = 0.5, B = 3)

#EM with regression
tau.em = survSensitivity(survdata$t, survdata$d, survdata$Z, survdata$X,
"EM_reg", zetaT = 0.5, zetaZ = 0.5, Bem = 50)