AUC_Bailers_method: Calculate AUC for Sparse Data

In ubiquity: PKPD, PBPK, and Systems Pharmacology Modeling Tools

Calculate AUC for Sparse Data

Description

This is an implementation of Bailors method for calculating AUCs with sparse sampling. It is taken from the following publication:

Nedelman, J. R., Gibiansky, E., & Lau, D. T. (1995). Applying Bailer's method for AUC confidence intervals to sparse sampling Pharmaceutical Research, 12(1), 124-128.

Usage

AUC_Bailers_method(
  conc_data = NULL,
  dsmap = list(NTIME = "NTIME", CONC = "CONC", ID = "ID")
)

Arguments

conc_data	data frame containing the sparse data
dsmap	list with names specifying the columns: NTIME Nominal time since last dose; "NTIME" (default) CONC Concentration data; "CONC" (default) ID Subject ID; ("ID" (default)

Value

list with the following elements

• isgood Boolean value indicating the result of the function call

• AUC Mean AUC

• var_AUC Variance of the AUC

• msgs Sequence of strings contianing a description of any problems

• obss Internal of observations

• times Sequence of time corresponding to the rows of obs

• r number of observations at each time point (rows correspond to rows of obs)

ubiquity documentation built on Nov. 2, 2023, 6:26 p.m.