Description Usage Arguments Details Value Author(s) References Examples

View source: R/comprSensitivity.R

`comprSensitivity`

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

1 2 |

`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(π)* (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:

*λ_j (t | Z, X, U) = λ_{j0} (t) exp( τ_j Z + X' β_j + ζ_j U).*

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

*P(Z=1 | X, U) = Φ(X' β_z + ζ_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.

1 2 3 4 5 6 7 8 9 | ```
#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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