Description Usage Arguments Details Value Author(s) References Examples

This function allows for the estimation of time-dependent ROC curve by taking into account possible confounding factors. Two methods are implemented: i) the standardized and weighted ROC based on an IPW estimator, and ii) the placement values ROC.

1 2 | ```
adjusted.ROCt(times, failures, variable, confounders, database,
pro.time, precision)
``` |

`times` |
A character string with the name of the variable in |

`failures` |
A character string with the name of the variable in |

`variable` |
A character string with the name of the variable in |

`confounders` |
An object of class "formula". More precisely only the right part with an expression of the form |

`database` |
An object of the class |

`pro.time` |
The value of prognostic time represents the maximum delay for which the capacity of the variable is evaluated. The same unit than the one used in the argument |

`precision` |
The quintiles (between 0 and 1) of the prognostic variable used for computing each point of the time dependent ROC curve. 0 (min) and 1 (max) are not allowed. |

This function computes confounder-adjusted time-dependent ROC curve with right-censored data. We adapted the naive IPCW estimator as explained by Blanche, Dartigues and Jacqmin-Gadda (2013) by considering the probability of experiencing the event of interest before the fixed prognostic time, given the possible confounding factors.

`table` |
This data frame presents the sensitivities and specificities associated with the cut-off values. |

`auc` |
The area under the time-dependent ROC curve for a prognostic up to |

Y. Foucher <Yohann.Foucher@univ-nantes.fr>

Blanche P, Dartigues J, Jacqmin-Gadda H. (2013) Review and comparison of roc curve estimators for a time-dependent outcome with marker-dependent censoring. Biometrical Journal, 55, 687-704. <doi:10.1002/bimj.201200045>

Le Borgne F. et al. (2017) Standardized and weighted time-dependent ROC curves to evaluate the intrinsic prognostic capacities of a marker by taking into account confounding factors. Manuscript submitted.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 | ```
# import and attach the data example
data(dataDIVAT)
# A subgroup analysis to reduce the time needed for this exemple
dataDIVAT <- dataDIVAT[1:400,]
# The standardized and weighted time-dependent ROC curve to evaluate the
# capacities of the recipient age for the prognosis of post kidney
# transplant mortality up to 2000 days by taking into account the
# donor age and the recipient gender.
# 1. Standardize the marker according to the covariates among the controls
lm1 <- lm(ageR ~ ageD + sexeR, data=dataDIVAT[dataDIVAT$death.time >= 2500,])
dataDIVAT$ageR_std <- (dataDIVAT$ageR - (lm1$coef[1] + lm1$coef[2] * dataDIVAT$ageD +
lm1$coef[3] * dataDIVAT$sexeR)) / sd(lm1$residuals)
# 2. Compute the sensitivity and specificity from the proposed IPW estimators
roc2 <- adjusted.ROCt(times="death.time", failures="death", variable="ageR_std",
confounders=~bs(ageD, df=3) + sexeR, database=dataDIVAT, pro.time=2000,
precision=seq(0.1,0.9, by=0.2))
# The corresponding ROC graph
plot(1-roc2$table$sp, roc2$table$se, ylim=c(0,1), xlim=c(0,1), ylab="sensitivity",
xlab="1-specificity", type="l", lty=1, col=1, lwd=2)
abline(c(0,0), c(1,1), lty=2)
# The corresponding AUC
roc2$auc
``` |

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