View source: R/comprSensitivity.R

comprSensitivity | R Documentation |

`comprSensitivity`

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

```
comprSensitivity(t, d, Z, X, method, zetaT = seq(-2,2,by=0.5),
zetat2 = 0, zetaZ = seq(-2,2,by=0.5), theta = 0.5, B = 50, Bem = 200)
```

`t` |
survival outcomes with competing risks. |

`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 |

`zetat2` |
value 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 of the jth type of failure is modeled using the Cox proportional hazards (PH) regression:

`\lambda_j (t | Z, X, U) = \lambda_{j0} (t) exp( \tau_j Z + X' \beta_j + \zeta_j U).`

Given `X`

and `U`

, `Z`

follows a generalized linear model:

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

`tau1` |
a data.frame with zetaz, zetat1, zetat2, tau1, tau1.se and t statistic in the event of interest response model. |

`tau2` |
a data.frame with zetaz, zetat, zetat2, tau2, tau2.se and t statistic in the competing risks response model. |

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(comprdata)
#stochastic EM with regression
tau.sto = comprSensitivity(comprdata$t, comprdata$d, comprdata$Z, comprdata$X,
"stoEM_reg", zetaT = 0.5, zetaZ = 0.5, B = 3)
#EM with regression
tau.em = comprSensitivity(comprdata$t, comprdata$d, comprdata$Z, comprdata$X,
"EM_reg", zetaT = 0.5, zetaZ = 0.5, Bem = 50)
```

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