lee {PK} | R Documentation |
Estimation of initial and terminal half-life by two-phase linear regression fitting.
lee(time, conc, points=3, prev=0, method=c("lad", "ols", "hub", "npr"), lt=TRUE)
time |
time points of concentration assessments. |
conc |
levels of concentrations. |
points |
minimum number of data points in the terminal phase. |
prev |
pre-dosing value. |
method |
method of model fitting. |
lt |
requesting a longer terminal than initial half-life. |
Estimation of initial and terminal half-life based on the method of Lee et al. (1990). This method uses a two-phase linear regression approach separate the model into two straight lines based on the selection of the log10 transformed concentration values. For two-phase models the initial and terminal half-life were determined from the slopes of the regression lines. If a single-phase model is selected by this method, the corresponding half-life is utilized as both initial and terminal phase half-life. Half-life is determined only for decreasing initial and terminal phases.
The method ols
uses the ordinary least squares regression (OLS) to fit regression lines.
The method lad
uses the absolute deviation regression (LAD) to fit regression lines by using the algorithm as described in Birkes and Dodge (chapter 4, 1993) for calculation of regression estimates.
The method hub
uses the Huber M regression to fit regression lines. Huber M-estimates are calculated by non-linear estimation using the function optim
, where OLS regression parameters are used as starting values. The function that is minimized involved k = 1.5*1.483*MAD, where MAD is defined as the median of absolute deviation of residuals obtained by a least absolute deviation (LAD) regression based on the observed data. The initial value of MAD is used and not updated during iterations (Holland and Welsch, 1977).
The method npr
uses the nonparametric regression to fit regression lines by using the algorithm as described in Birkes and Dodge (chapter 6, 1993) for calculation of regression estimates.
The selection criteria for the best tuple of regression lines is the sum of squared residuals for the ols
method, the sum of Huber M residuals for the hub
method, the sum of absolute residuals for the lad
method and the sum of a function on ranked residuals for the npr
method (see Birkes and Dodge (page 115, 1993)). Calculation details can be found in Wolfsegger (2006).
When lt=TRUE
, the best two-phase model where terminal half-life >= initial half-life is selected. When lt=FALSE
, the best two-phase model among all possible tuples of regression is selected which can result in longer initial half-life than terminal half-life.
If the pre-dosing value indicating the intrinsic level is greater than 0, the pre-dosing value is subtracted from all concentration levels before calculation of initial and terminal half-life.
A list of S3 class "halflife"
containing the following components:
parms |
half-life and model estimates. |
chgpt |
change point between initial and terminal phase. |
time |
time points of concentration assessments. |
conc |
levels of concentrations. |
method |
"lee". |
Records including missing values and concentration values below or equal to zero are omitted.
Martin J. Wolfsegger and Thomas Jaki
Birkes D. and Dodge Y. (1993). Alternative Methods of Regression. Wiley, New York, Chichester, Brisbane, Toronto, Singapore.
Holland P. W. and Welsch R. E. (1977). Robust regression using iteratively reweighted least-squares. Commun. Statist.-Theor. Meth. A6(9):813-827.
Lee M. L., Poon Wai-Yin, Kingdon H. S. (1990). A two-phase linear regression model for biologic half-life data. Journal of Laboratory and Clinical Medicine. 115(6):745-748.
Wolfsegger M. J. (2006). The R Package PK for Basic Pharmacokinetics. Biometrie und Medizin, 5:61-68.
## example for preparation 1 from Lee et al. (1990) time <- c(0.5, 1.0, 4.0, 8.0, 12.0, 24.0) conc <- c(75, 72, 61, 54, 36, 6) result1 <- lee(conc=conc, time=time, method='ols', points=2, lt=TRUE) print(result1$parms) plot(result1) plot(result1, log='y') ## example for preparation 1 from Lee et al. (1990) time <- c(0.5, 1.0, 4.0, 8.0, 12.0, 24.0) conc <- c(75, 72, 61, 54, 36, 6) result2 <- lee(conc=conc, time=time, method='ols', points=2, lt=FALSE) print(result2$parms) plot(result2) plot(result2, log='y') ## example for preparation 2 from Lee et al. (1990) time <- c(0.5, 1.0, 2.0, 6.5, 8.0, 12.5, 24.0) conc <- c(75, 55, 48, 51, 39, 9, 5) result3 <- lee(conc=conc, time=time, method='ols', points=2, lt=FALSE) print(result3$parms) plot(result3) plot(result3, log='y') ## advanced plots xlim <- c(0,30) ylim <- c(1,80) ylab <- 'Log Concentration' xlab <- 'Time [hours]' text1 <- paste('Initial half-life:', round(result2$parms[1,1],2), ' Terminal half-life:', round(result2$parms[1,2],2)) text2 <- paste('Initial half-life:', round(result3$parms[1,1],2), ' Terminal half-life:', round(result3$parms[1,2],2)) split.screen(figs=c(2,1)) screen(1) plot(result2, ylab=ylab, xlab=xlab, main='Half-life: Preparation 1', xlim=xlim, ylim=ylim, log='y', sub=text1) screen(2) plot(result3, ylab=ylab, xlab=xlab, main='Half-life: Preparation 2', xlim=xlim, ylim=ylim, log='y', sub=text2) close.screen(all=TRUE) ## artificial example time <- c(5, 180, 360, 540, 720)/60 conc <- c(2.360, 0.061, 0.019, 0.012, 0.024) result4 <- lee(conc=conc, time=time, method='lad', points=2, lt=FALSE) print(result4) plot(result4) plot(result4, log='y')