Cutoff value calculation using method described in Aizenstein et al. 2008

See the reference below and the tacmagic walkthrough vignette. Aizenstein et al. (2008) proposed a standardized method of calculating Pittsburgh Compound B (PIB) cutoff values to classify participants as PIB+ or PIB-. They used the distribution volume ratio (DVR) from several ROIs associated with amyloid deposition. The steps are summarized below. cutoff_aiz() implements 1-3, returning cutoff values for each ROI. It can be used to dichotomize participants, with pos_anyroi().

Usage

cutoff_aiz(modelstats, ROIs)

Arguments

modelstats

SUVR or DVR data for group of participants from batch_tm()

ROIs

list of variables (ROIs) to use for cutoff detection

Value

Cutoff values for each ROI based on the above method

Details

1. Remove outliers from a group of cognitively normal individuals. An outlier is defined as having any ROI with DVR > upper inner fence of that ROI (= 3rd quartile + (1.5 * IQR). 2. Iterate step 1 as needed until there are no more outlying participants. 3. From this subset of the group with outliers removed, the cutoff value for each ROI is set as the upper inner fence. 4. For all participants, if there is any ROI above the cutoff for that region, then the participant is deemed to be PIB+.

References

Aizenstein HJ, Nebes RD, Saxton JA, et al. 2008. Frequent amyloid deposition without significant cognitive impairment among the elderly. Arch Neurol 65: 1509-1517.

See also

Other Cutoff functions: pos_anyroi

Examples

cutoff_aiz(fake_DVR, c("ROI1_DVR", "ROI2_DVR", "ROI3_DVR", "ROI4_DVR"))
#> Iteration: 1 Removed: 10
#> Iteration: 2 Removed: 1
#> Iteration: 3 Removed: 0
#> ROI1_DVR ROI2_DVR ROI3_DVR ROI4_DVR 
#> 1.370899 1.332725 1.330504 1.273470