Description Usage Arguments Value References Examples

View source: R/survAM.estimate.R

This function estimates the AUC, TPR(c), FPR(c), PPV(c), and NPV(c) for for a specific timepoint and marker cutoff value c using semiparametric or nonparametric estimates. Standard errors, and confidence intervals are also computed. Either analytic or bootstrap standard errors can be computed.

1 2 3 4 5 6 7 8 9 | ```
survAM.estimate(time, event, marker,
data,
predict.time,
marker.cutpoint = 'median',
estimation.method = "IPW",
ci.method = "logit.transformed",
se.method = "bootstrap",
bootstraps = 1000,
alpha=0.05)
``` |

`time` |
time to event variable |

`event` |
indicator for the status of event of interest. event = 0 for censored observations, and event = 1 for event of interest. |

`marker` |
marker variable of interest |

`data` |
data frame in which to look for input variables. |

`predict.time` |
numeric value of the timepoint of interest for which to estimate the risk measures |

`marker.cutpoint` |
numeric value indicating the value of the
cutpoint 'c' at which to estimate 'FPR', 'TPR', 'NPV' or
'PPV'. default is |

`estimation.method` |
Either "IPW" for non-parametric IPW estimates (default) or "Cox" for semi-parametric estimates that use a Cox proportional hazards model. |

`ci.method` |
character string of either 'logit.transformed' (default) or 'standard' indicating whether normal approximated confidence intervals should be calculated using logistic transformed values or the standard method. |

`se.method` |
Method to calculate standard errors for estimates. Options are "bootstrap" (default) or "asymptotic". Asymptotic estimates are based on large sample calculations and will not hold in small samples. Please see referenced papers for more information. |

`bootstraps` |
if se.method = 'bootstrap', number of bootstrap replicates to use to estimate the SE. |

`alpha` |
alpha value for confidence intervals. (1-alpha)*100 is alpha = 0.05. |

a list with components

`estimates` |
point estimates for risk measures |

`se` |
standard errors for estimates |

`CIbounds` |
bounds for (1-alpha)*100 confidence interval |

`model.fit` |
if ESTmethod = "SP", object of type
'coxph' from fitting the model |

```
cutoff, CImethod, SEmethod,
predict.time, alpha
``` |
function inputs |

Liu D, Cai T, Zheng Y. Evaluating the predictive value of biomarkers with stratified case-cohort design. Biometrics 2012, 4: 1219-1227.

Pepe MS, Zheng Y, Jin Y. Evaluating the ROC performance of markers for future events. Lifetime Data Analysis. 2008, 14: 86-113.

Zheng Y, Cai T, Pepe MS, Levy, W. Time-dependent predictive values of prognostic biomarkers with failure time outcome. JASA 2008, 103: 362-368.

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 28 29 30 31 32 33 34 | ```
data(SimData)
#non-parametric estimates
tmp <- survAM.estimate(time =survTime,
event = status,
marker = Y,
data = SimData,
estimation.method = "IPW",
predict.time = 2,
marker.cutpoint = 0,
bootstraps = 50)
tmp
tmp$estimates
#semi-parametric estimates
tmp <- survAM.estimate(time =survTime,
event = status,
marker = Y,
data = SimData,
estimation.method = "Cox",
predict.time = 2,
marker.cutpoint = 0,
bootstraps = 50)
#semi-parametric estimates with asymptotic standard errors
tmp <- survAM.estimate(time =survTime,
event = status,
marker = Y,
data = SimData,
estimation.method = "Cox",
se.method = "asymptotic",
predict.time = 2,
marker.cutpoint = 0,
bootstraps = 50)
``` |

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