bicomprisk | R Documentation |

Estimation of concordance in bivariate competing risks data

bicomprisk( formula, data, cause = c(1, 1), cens = 0, causes, indiv, strata = NULL, id, num, max.clust = 1000, marg = NULL, se.clusters = NULL, wname = NULL, prodlim = FALSE, messages = TRUE, model, return.data = 0, uniform = 0, conservative = 1, resample.iid = 1, ... )

`formula` |
Formula with left-hand-side being a |

`data` |
Data frame |

`cause` |
Causes (default (1,1)) for which to estimate the bivariate cumulative incidence |

`cens` |
The censoring code |

`causes` |
causes |

`indiv` |
indiv |

`strata` |
Strata |

`id` |
Clustering variable |

`num` |
num |

`max.clust` |
max number of clusters in timereg::comp.risk call for iid decompostion, max.clust=NULL uses all clusters otherwise rougher grouping. |

`marg` |
marginal cumulative incidence to make stanard errors for same clusters for subsequent use in casewise.test() |

`se.clusters` |
to specify clusters for standard errors. Either a vector of cluster indices or a column name in |

`wname` |
name of additonal weight used for paired competing risks data. |

`prodlim` |
prodlim to use prodlim estimator (Aalen-Johansen) rather than IPCW weighted estimator based on comp.risk function.These are equivalent in the case of no covariates. These esimators are the same in the case of stratified fitting. |

`messages` |
Control amount of output |

`model` |
Type of competing risk model (default is Fine-Gray model "fg", see comp.risk). |

`return.data` |
Should data be returned (skipping modeling) |

`uniform` |
to compute uniform standard errors for concordance estimates based on resampling. |

`conservative` |
for conservative standard errors, recommended for larger data-sets. |

`resample.iid` |
to return iid residual processes for further computations such as tests. |

`...` |
Additional arguments to timereg::comp.risk function |

Thomas Scheike, Klaus K. Holst

Scheike, T. H.; Holst, K. K. & Hjelmborg, J. B. Estimating twin concordance for bivariate competing risks twin data Statistics in Medicine, Wiley Online Library, 2014 , 33 , 1193-204

library("timereg") ## Simulated data example prt <- simnordic.random(2000,delayed=TRUE,ptrunc=0.7, cordz=0.5,cormz=2,lam0=0.3) ## Bivariate competing risk, concordance estimates p11 <- bicomprisk(Event(time,cause)~strata(zyg)+id(id),data=prt,cause=c(1,1)) p11mz <- p11$model$"MZ" p11dz <- p11$model$"DZ" par(mfrow=c(1,2)) ## Concordance plot(p11mz,ylim=c(0,0.1)); plot(p11dz,ylim=c(0,0.1)); ## entry time, truncation weighting ### other weighting procedure prtl <- prt[!prt$truncated,] prt2 <- ipw2(prtl,cluster="id",same.cens=TRUE, time="time",cause="cause",entrytime="entry", pairs=TRUE,strata="zyg",obs.only=TRUE) prt22 <- fast.reshape(prt2,id="id") prt22$event <- (prt22$cause1==1)*(prt22$cause2==1)*1 prt22$timel <- pmax(prt22$time1,prt22$time2) ipwc <- timereg::comp.risk(Event(timel,event)~-1+factor(zyg1), data=prt22,cause=1,n.sim=0,model="rcif2",times=50:90, weights=prt22$weights1,cens.weights=rep(1,nrow(prt22))) p11wmz <- ipwc$cum[,2] p11wdz <- ipwc$cum[,3] lines(ipwc$cum[,1],p11wmz,col=3) lines(ipwc$cum[,1],p11wdz,col=3)

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