ROC | R Documentation |

`joint`

model.Using longitudinal information available up to a time, establish diagnostic capabilities (ROC, AUC and Brier score) of a fitted joint model.

```
ROC(fit, data, Tstart, delta, control = list(), progress = TRUE, boot = FALSE)
```

`fit` |
a joint model fit by the |

`data` |
the data to which the original |

`Tstart` |
The start of the time window of interest, |

`delta` |
scalar denoting the length of time interval to check for failure times. |

`control` |
list of control arguments to be passed to |

`progress` |
should a progress bar be shown, showing the current progress of the ROC
function (
to |

`boot` |
logical. Not currently used, legacy argument. |

A list of class `ROC.joint`

consisting of:

`Tstart`

numeric denoting the start of the time window of interest; all dynamic predictions generated used longitudinal information up-to time

`T_{\mathrm{start}}`

.`delta`

scalar which denotes length of interval to check, such that the window is defined by

`[T_{\mathrm{start}}, T_{\mathrm{start}}, + \delta]`

.`candidate.u`

candidate vector of failure times to calculate dynamic probability of surviving for each subject alive in

`data`

at time`T_{\mathrm{start}}`

.`window.failures`

numeric denoting the number of observed failures in

`[T_{\mathrm{start}}, T_{\mathrm{start}}, + \delta]`

.`Tstart.alive`

numeric denoting the risk set at

`Tstart`

.`metrics`

a

`data.frame`

containing probabilistic`thresholds`

with:`TP`

true positives;`FN`

false negatives;`FP`

false positives;`TN`

true negatives;`TPR`

true positive rate (sensitivity);`FPR`

false positive rate (1-specificity);`Acc`

accuracy;`PPV`

positive predictive value (precision);`NPV`

negative predictive value;`F1s`

F1 score and`J`

Youden's J statistic.- AUC
the area under the curve.

- BrierScore
The Brier score.

- PE
The predicted error (taking into account censoring), loss function: square.

- MH.acceptance
Raw acceptance percentages for each subject sampled.

- MH.acceptance.bar
mean acceptance of M-H scheme across all subjects.

- simulation.info
list containing information about call to

`dynPred`

.

James Murray (j.murray7@ncl.ac.uk).

`dynPred`

, and `plot.ROC.joint`

.

```
data(PBC)
PBC$serBilir <- log(PBC$serBilir)
long.formulas <- list(serBilir ~ drug * time + (1 + time|id))
surv.formula <- Surv(survtime, status) ~ drug
family <- list('gaussian')
fit <- joint(long.formulas, surv.formula, PBC, family)
(roc <- ROC(fit, PBC, Tstart = 8, delta = 2, control = list(nsim = 25)))
plot(roc)
```

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