MAUCtest {NIRStat}

MAUC statistics based Analysis for NIRS time series.

Description

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

Usage

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

Arguments

Yvec

The outcome of NIRS time series Y(t_{i}) of length N ranging from 15 to 100.

timevec

The time index of NIRS time series t_{i} of length N.

transfusionvec

The 0/1 indicator of the transfusion status X(t_{i}). X(t_{i})=0 means the current time point is before transfusion and X(t_{i})=1 means the current time point is after transfusion.

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.

Details

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.

Value

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_est = TRUE.

SD_post

SD of the MAUC statistic for post-transfusion. Optional, only when SD_est = TRUE.

Author(s)

Yikai Wang [Emory], Xiao Wang [ICF]
Maintainer: Yikai Wang [e-mail]

References

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.

Examples

# 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)