brace {BRACE} | R Documentation |

`brace`

is used to estimate the treatment effect with adjusted
confounders on the composite hazard
for primary or competing events, and adjust for bias from residual
confounding in non-randomized data by BRACE method

```
brace(
ftime,
fstatus,
covs = NA,
trt,
failcode = 1,
cencode = 0,
PS = 0,
B = 1000
)
```

`ftime` |
vector of failure/censoring times |

`fstatus` |
vector with a unique code for each failure type and a separate code for censored observations (default is primary event = 1, competing event = 2, censored = 0) |

`covs` |
matrix (nobs x ncovs) of fixed covariates. If no covariates, set covs = NA (default is NA) |

`trt` |
vector of treatment indicator (1 for treatment group) |

`failcode` |
code of fstatus that denotes the failure type of interest |

`cencode` |
code of fstatus that denotes censored observations |

`PS` |
whether to use propensity score method for adjusting the confounding effect (1 for propensity score method, default is 0) |

`B` |
bootstrap sample size for calculating the Confidence interval, default is 1000 |

a list of class `brace`

, with components:

`$Summary` |
summary table of BRACE method |

`$`BRACE HR Distribution`` |
the estimated regression coefficients in each bootstrap sample |

`$`Omega Estimate`` |
estimate of relative hazards for primary events vs. combined events |

`$Epsilon` |
the estimated bias |

`$`Combined Endpoint Model`` |
the regression model for combined events |

`$`Primary Endpoint Model`` |
the regression model for primary events |

`$`Competing Endpoint Model`` |
the regression model for competing events |

`$`Omega Curve`` |
estimate of omega over time |

`$`Combined Endpoint Curve`` |
survival curve for combined events |

`$`Primary Endpoint Curve`` |
survival curve for primary events |

`$`Competing Endpoint Curve`` |
survival curve for competing events |

Williamson, Casey W., et al. "Bias Reduction through Analysis of Competing Events (BRACE) Correction to Address Cancer Treatment Selection Bias in Observational Data." Clinical Cancer Research 28.9 (2022): 1832-1840.

```
nsims = 1; nobs = 1500
f = 0.5; g = 0.333; b = 8; w1 = w2 = 0.667
theta1 = 0.5; theta2 = 1; omegaplus = 1; k3 = 0.333
sim1 = gendat(nsims,nobs,f,g,b,w1,w2,omegaplus,theta1,theta2,k3)
ftime = sim1$time
fstatus = sim1$pfs_ci
covs = NA
trt = sim1$group
braceoutput = brace(ftime, fstatus, covs, trt, PS=0, B=10)
nsims = 1; nobs = 1500
f1 = f2 = 0.5; g = 0.333; b1 = 8; b2 = 4; w1 = w2 = 0.667
theta1 = 0.5; theta2 = 1; omegaplus = 1; k3 = 0.333
sim1 = gendat2(nsims,nobs,f1,f2,g,b1,b2,w1,w2,omegaplus,theta1,theta2,k3)
ftime = sim1$time
fstatus = sim1$pfs_ci
covs = sim1$factor2
trt = sim1$group
braceoutput = brace(ftime, fstatus, covs, trt, PS=1, B=10)
```

[Package *BRACE* version 0.1.0 Index]