Description Usage Arguments Details Value Author(s) References Examples

Estimate the slope statistics and conduct a nonparametric based test on the slope difference before transfuion and after trasfusion. If detection limit occurs at 15

1 | ```
Slopetest(Yvec,timevec,transfusionvec,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 |

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

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

This function estimates the slope statistics before transfusion and after transfusion based on penalized regression spline method and tests the difference based on a within-band permutation approach. If there is detection limit occurs (15), it will impute the missed data based on a uniform distribution and estimate the slope statistics through a standard imputation approach. The statistical testing is conducted through a nested within-band permutation approach across all imputated datasets.

An R vector from Slopetest containing Slope statistics and Pvalue in the following order:

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

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

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

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

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

`SD_post` |
SD of the Slope 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 Slope statistics of the NIRS data and test on the difference.
Slopetest(dat$Y,dat$t,dat$trans,FALSE,100)
``` |

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