Percent exceeding the trend (PET)

The pet function returns the percentage of Phase B data points that exceed the prediction based on the Phase A trend. A binomial test against a 50/50 distribution is calculated. It also calculates the percentage of Phase B data points that exceed the upper (or lower) 95 percent confidence interval of the predicted progression.

Usage

pet(data, dvar, pvar, mvar, ci = 0.95, decreasing = FALSE, phases = c(1, 2))

Arguments

data

A single-case data frame. See scdf() to learn about this format.

dvar

Character string with the name of the dependent variable. Defaults to the attributes in the scdf file.

pvar

Character string with the name of the phase variable. Defaults to the attributes in the scdf file.

mvar

Character string with the name of the measurement time variable. Defaults to the attributes in the scdf file.

ci

Width of the confidence interval. Default is ci = 0.95.

decreasing

If you expect data to be lower in the B phase, set decreasing = TRUE. Default is decreasing = FALSE.

phases

A vector of two characters or numbers indicating the two phases that should be compared. E.g., phases = c("A","C") or phases = c(2,4) for comparing the second to the fourth phase. Phases could be combined by providing a list with two elements. E.g., phases = list(A = c(1,3), B = c(2,4)) will compare phases 1 and 3 (as A) against 2 and 4 (as B). Default is phases = c(1,2).

Value

PET	Percent exceeding the trend.
ci	Width of confidence interval.
decreasing	Logical argument from function call (see Arguments above).

Details

The PET is calculated by first fitting a linear model to the Phase A data to estimate the trend. Then, for each data point in Phase B, it is determined whether it exceeds the predicted value from the Phase A trend. The PET is the percentage of Phase B data points that exceed this predicted value. Additionally, a binomial test is performed to assess whether the observed PET is significantly greater than what would be expected by chance (i.e., 50%).

See also

Other overlap functions: ird(), nap(), overlap(), pand(), pem(), pnd(), print.sc_cdc(), select_phases(), tau_u()

Author

Juergen Wilbert

Examples

## Calculate the PET and use a 99%-CI for the additional calculation
# create random example data
design <- design(n = 5, slope = 0.2)
dat <- random_scdf(design, seed = 23)
pet(dat, ci = .99)
#> Percent Exceeding the Trend
#> 
#> 
#>       Case   PET PET CI  binom.p
#>  [case #1]  13.3    0.0 1.00e+00
#>  [case #2] 100.0   53.3 3.05e-05
#>  [case #3] 100.0   93.3 3.05e-05
#>  [case #4]   0.0    0.0 1.00e+00
#>  [case #5]  80.0    0.0 1.76e-02
#> 
#> Binom.test: alternative hypothesis: true probability > 50%
#> PET CI: Percent of values greater than upper 99% confidence threshold (single sided)