Description Usage Arguments Details Value Author(s) References Examples

Estimate the Mean Area Under the Curve (MAUC) statistics and conduct a nonparametric test on the MAUC difference before transfuion and after trasfusion. If detection limit occurs at 15

1 | ```
MAUCtest(Yvec,timevec,transfusionvec,fig = T,SD_est=F,num.permu=1000)
``` |

`Yvec` |
The outcome of NIRS time series |

`timevec` |
The time index of NIRS time series |

`transfusionvec` |
The 0/1 indicator of the transfusion status |

`fig` |
Whether to plot the NIRS time series. Default value is TRUE. |

`SD_est` |
Whether to estimate the SD of the MAUC statistic for pre-transfusion and post-transfuion. Default value is FALSE. |

`num.permu` |
Number of permutation for permutation test. Default value is 1000. |

This functinon estimates the Mean Area Under the Curve (MAUC) statistics and conducts a permutation based test on the MAUC difference before transfuion and after trasfusion. If detection limit (DL) occurs (15), it will impute the missed data based on a uniform distribution and estimate the MAUC statistics through a standard imputation approach. The statistical testing is conducted through a nested permutation approach across all imputated datasets.

An R vector from MAUCtest containing MAUC statistics and Pvalue in the following order:

`MAUC.before` |
The estimated MAUC statistic before transfusion. |

`MAUC.after` |
The estimated MAUC statistic after transfusion. |

`MAUC.diff` |
The estimated MAUC statistic difference between before transfusion and after transfusion. |

`Pvalue` |
The pvalue of testing the MAUC difference to be zero or not. |

`SD_pre` |
SD of the MAUC statistic for pre-transfusion. Optional, only when |

`SD_post` |
SD of the MAUC statistic for post-transfusion. Optional, only when |

Yikai Wang [Emory], Xiao Wang [ICF]

Maintainer: Yikai Wang johnzon.wyk@gmail.com

Guo, Y., Wang, Y., Marin, T., Kirk, E., Patel, R., Josephson, C. *
Statistical methods for characterizing transfusion-related changes in regional oxygenation using Near-infrared spectroscopy in preterm infants.
* Statistical methods in medical research 28.9 (2019): 2710-2723.

1 2 3 4 5 6 7 8 9 | ```
# Data Simulation
dat = data.frame(Y= rep(0,100),t=1:100,trans = c(rep(0,50),rep(1,50)))
dat$Y = apply(dat,1,function(x){rnorm(1,5*rnorm(1),6*exp(rnorm(1)))})
dat$Y = dat$Y + 15 - quantile(dat$Y,0.3)
dat$Y[dat$Y<=15] = 15
# Estimate the MAUC statistics of the NIRS data and test on the difference.
MAUCtest(dat$Y,dat$t,dat$trans,TRUE,FALSE,100)
``` |

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