Percentage exceeding the median — PEM • SingleCaseES

Calculates the percentage exceeding the median (PEM) index (Ma, 2006).

PEM(
  A_data,
  B_data,
  condition,
  outcome,
  baseline_phase = NULL,
  intervention_phase = NULL,
  improvement = "increase"
)

Arguments

A_data: vector of numeric data for A phase. Missing values are dropped.
B_data: vector of numeric data for B phase. Missing values are dropped.
condition: vector identifying the treatment condition for each observation in the series.
outcome: vector of outcome data for the entire series.
baseline_phase: character string specifying which value of condition corresponds to the baseline phase. Defaults to first observed value of condition.
intervention_phase: character string specifying which value of condition corresponds to the intervention phase. Defaults to second unique value of condition.
improvement: character string indicating direction of improvement. Default is "increase".

Value

Numeric value

Details

For an outcome where increase is desirable, PEM is calculated as the proportion of observations in the B phase that exceed the median observation from the A phase. For an outcome where decrease is desirable, PEM is calculated as the proportion of observations in the B phase that are less than the median observation from the A phase. Ties are counted with a weight of 0.5. The range of PEM is [0,1].

References

Ma, H.-H. (2006). An alternative method for quantitative synthesis of single-subject researches: Percentage of data points exceeding the median. Behavior Modification, 30(5), 598--617. doi:doi:10.1177/0145445504272974

Examples

A <- c(20, 20, 26, 25, 22, 23)
B <- c(28, 25, 24, 27, 30, 30, 29)
PEM(A_data = A, B_data = B)
#>    ES Est
#> 1 PEM   1