drc (version 1.9-0)

ursa: Model function for the universal response surface approach (URSA) for the quantitative assessment of drug interaction

Description

URSA provides a parametric approach for modelling the joint action of several agents. The model allows quantification of synergistic effects through a single parameter.

Usage

ursa(fixed = c(NA, NA, NA, NA, NA, NA, NA), names = c("b1", "b2", "c", "d", "e1", "e1", "f"), ssfct = NULL)

Arguments

fixed

numeric vector. Specifies which parameters are fixed and at what value they are fixed. NAs for parameter that are not fixed.

names

a vector of character strings giving the names of the parameters. The default is reasonable.

ssfct

a self starter function to be used (optional).

Value

• A list containing the nonlinear function, the self starter function, and the parameter names.

Details

The model function is defined implicitly through an appropriate equation. The details are found in Syracuse and Greco (1986), Greco et al (1990, 1995).

References

Finney, D. J. (1979) Bioassay and the Practise of Statistical Inference, Int. Statist. Rev., 47, 1--12. Syracuse, K. C. and Greco, W. R. (1986) Comparison between the method of Chou and Talalay and a new method for the assessment of the combined effects of drugs: A Monte-Carlo simulation study, Proceedings of the Biopharmaceutical Section of the American Statistsical Association, 127--132. Greco, W. R. and Park H. S. and Rustum, Y. M. (1990) Application of a New Approach for the Quantitation of Drug Synergism to the Combination of cis-Diamminedichloroplatinum and 1-beta-D-Arabinofuranosylcytosine, Cancer Research, 50, 5318--5327. Greco, W. R. Bravo, G. and Parsons, J. C. (1995) The Search for Synergy: A Critical Review from a Response Surface Perspective, Pharmacological Reviews, 47, Issue 2, 331--385.

See Also

Other models for fitting mixture data are the Hewlett and Voelund models mixture, ~~~

Examples

## Data from Greco et al (1995)
d1 <- c(0, 0, 0, 0, 0, 0, 0, 0, 2, 5, 10, 20, 50, 2, 2, 2, 2, 2, 5, 5, 5, 5, 5,10, 10, 10, 10, 10, 20, 20, 20, 20, 20, 50, 50, 50, 50, 50)

d2 <- c(0, 0, 0, 0.2, 0.5, 1, 2, 5, 0, 0, 0, 0, 0, 0.2, 0.5, 1, 2, 5, 0.2, 0.5, 1, 2, 5, 0.2, 0.5, 1, 2, 5, 0.2, 0.5, 1, 2, 5, 0.2, 0.5, 1, 2, 5)

effect <- c(106.00, 99.20, 115.00, 79.20, 70.10, 49.00, 21.00, 3.83, 74.20, 71.50, 48.10, 30.90, 16.30, 76.30, 48.80, 44.50, 15.50, 3.21, 56.70, 47.50,
26.80, 16.90, 3.25, 46.70, 35.60, 21.50, 11.10, 2.94, 24.80, 21.60, 17.30, 7.78, 1.84, 13.60, 11.10, 6.43, 3.34, 0.89)

greco <- data.frame(d1, d2, effect)

## Fitting the URSA model
greco.m1<-drm(effect~d1, data=greco, fct=ursa(fixed=c(NA,NA,0,NA,NA,NA,NA)), pmodels=list(~1,~1,~1,~I(1/d1)-1,~I(1/d2)-1,~1))

plot(fitted(greco.m1), residuals(greco.m1))
summary(greco.m1)

## Fitting the URSA model using values from Greco et al (1995) (p. 364)
greco.m2<-drm(effect~d1, data=greco, fct=ursa(fixed=c(NA,NA,0,NA,NA,NA,NA)), pmodels=list(~1,~1,~1,~I(1/d1)-1,~I(1/d2)-1,~1), start=c(-1.05, -2.04,95.1,11.1,1.07,0.52))
# same fit as above

## Adjusting for variance heterogeneity
greco.m3<-boxcox(greco.m2, method="anova")

plot(fitted(greco.m3), residuals(greco.m3))  # improved, not great
summary(greco.m3)

## Getting closer to Greco's estimates
#greco.m4<-drm(effect~d1, data=greco, fct=ursa(fixed=c(NA,NA,0,NA,NA,NA,NA)), pmodels=list(~1,~1,~1,~I(1/d1)-1,~I(1/d2)-1,~1), bcVal=0)  # not perfect
greco.m4<-drm(effect~d1, data=greco, fct=ursa(fixed=c(NA,NA,0,NA,NA,NA,NA)), pmodels=list(~1,~1,~1,~I(1/d1)-1,~I(1/d2)-1,~1), bcVal=0, start=coef(greco.m3))

plot(fitted(greco.m4), residuals(greco.m4))  # better!

summary(greco.m4)