View source: R/survSensitivity.R
survSensitivity | R Documentation |
survSensitivity
performs a dual-parameter sensitivity analysis of treatment effect to unmeasured confounding in observational studies with survival outcomes.
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)
t |
survival outcomes. |
d |
indicator of occurrence of event, with |
Z |
indicator of treatment. |
X |
pre-treatment covariates that will be included in the model as measured confounders. |
method |
needs to be one of |
zetaT |
range of coefficient of |
zetaZ |
range of coefficient of |
theta |
marginal probability of |
B |
iteration in the stochastic EM algorithm. |
Bem |
iteration used to estimate the variance-covariance matrix in the EM algorithm. |
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).
tau |
a data.frame with zetaz, zetat, tau1, tau1.se and t statistic. |
Rong Huang
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.
#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)
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