Operating Characteristic (OC) Curves for the PPQ Plan using Tolerance Interval.

Source: R/ti_occurve.R

The function for plotting the OC curve to show the PPQ plan based on the specification test, given lower and upper specification limits.

ti_occurve(attr.name, attr.unit, Llim, Ulim, mu, sigma, n, n.batch, alpha,
coverprob, side, add.reference, NV)

Arguments

attr.name

user-defined attribute name
attr.unit

user-defined attribute unit
Llim

lower specification limit
Ulim

upper specification limit
mu

hypothetical mean of the attribute
sigma

hypothetical standard deviation of the attribute
n

sample size (number of locations) per batch
n.batch

number of batches for passing PPQ during validation
alpha

significant level for constructing the tolerance interval.
coverprob

coverage probability for constructing the tolerance interval
side

whether a 1-sided or 2-sided tolerance interval is required (determined by side = 1 or side = 2, respectively).
add.reference

logical; if TRUE, then add reference OC curves (Baseline and High Performance) in the plot.
NV

nominal volume for the specification test.

Value

OC curves for specification test and PPQ plan.

References

Burdick, R. K., LeBlond, D. J., Pfahler, L. B., Quiroz, J., Sidor, L., Vukovinsky, K., & Zhang, L. (2017). Statistical Applications for Chemistry, Manufacturing and Controls (CMC) in the Pharmaceutical Industry. Springer.

See also

ti_pp and rl_pp.

Author

Yalin Zhu

Examples

if (FALSE) {
ti_occurve(attr.name = "Sterile Concentration Assay", attr.unit="%",
mu=97, sigma=seq(0.1, 10, 0.1), Llim=95, Ulim=105, n=10, add.reference=TRUE)

ti_occurve(attr.name = "Sterile Concentration Assay", attr.unit="%",
mu=100, sigma=seq(0.1, 10, 0.1), Llim=95, Ulim=105, n=10, add.reference=TRUE)

ti_occurve(attr.name = "Extractable Volume", attr.unit = "% of NV=3mL",
Llim = 100, Ulim = Inf, mu=102.5, sigma=seq(0.2, 6 ,0.05), n=40,
alpha = 0.05, coverprob = 0.97, side=1, NV=3)

ti_occurve(attr.name = "Extractable Volume", attr.unit = "% of NV=3mL",
Llim = 100, Ulim = Inf, mu=102.5, sigma=seq(0.2, 6 ,0.05), n=40,
alpha = 0.05, coverprob = 0.992, side=1, NV=3)
}