This data set is from Schoket et al. (1991) and is discussed in Chapter 17 of "Design of Observational Studies", second edition. The data describe 25 aluminum production workers (w) and 25 controls (c) matched for age and smoking. The outcome is a measure of genetic damage, namely DNA adducts per 10^8 nucleotides. The data are used as an example in Chapter 17 of "Design of Observational Studies", where the data illustrate the possibility that some treated individuals are strongly affected by treatment, while others are unaffected, so the average treatment effect may be small, but the effect on affected individuals may be large and evident in data.
A data frame with 25 observations on the following 9 variables.
Pair number, 1 to 25.
Worker ID from Schoket et al. (1991).
Worker age in years
Worker cigarettes per day
Worker DNA adducts
Control ID from Schoket et al. (1991).
Control age in years
Control cigarettes per day
Control DNA adducts
The data are from Schoket et al. (1991). The data are used as an example in Chapter 17 of "Design of Observational Studies"", second edition.
Conover, W. J. and Salsburg, D. S. (1988) <doi:10.2307/2531906> "Locally most powerful tests for detecting treatment effects when only a subset of patients can be expected to respond to treatment". Biometrics, 189-196.
Rosenbaum, P. R. (2007) <doi:10.1111/j.1541-0420.2007.00783.x> "Confidence intervals for uncommon but dramatic responses to treatment". Biometrics, 63(4), 1164-1171.
Rosenbaum, P. R. (2011) <doi:10.1111/j.1541-0420.2010.01535.x> "A new U statistic with superior design sensitivity in matched observational studies". Biometrics, 67(3), 1017-1027.
Schoket, B., Phillips, D. H., Hewer, A. and Vincze, I. (1991) <doi:10.1016/0165-1218(91)90084-Y> "32P-postlabelling detection of aromatic DNA adducts in peripheral blood lymphocytes from aluminium production plant workers". Mutation Research/Genetic Toxicology, 260(1), 89-98.
Stephenson, W. R. (1981) <doi:10.1080/01621459.1981.10477749> "A general class of one-sample nonparametric test statistics based on subsamples". Journal of the American Statistical Association, 76(376), 960-966.
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data(schoket) attach(schoket) plot(sort(adductsc),sort(adductsw),ylim=c(0,6.4),xlim=c(0,6.4), xlab="DNA adducts for controls",ylab="DNA adducts for workers", main="Quantile-Quantile Plot") # Compare with Chapter 17 abline(0,1) # line of equality legend(4,1,lty=1,"x=y") boxplot(adductsw,adductsc,ylim=c(0,6.4),ylab="DNA adducts",names=c("Worker","Control")) d<-adductsw-adductsc senWilcox(d,gamma=1) senWilcox(d,gamma=1.5) # sensitive to gamma=1.5 senU(d,gamma=1) # U-statistic version of Wilcoxon's statistic senU(d,gamma=1.8) # Stephenson's statistic is obtained from senU() # by setting m1=m2=m senU(d,m1=5,m2=5,m=5,gamma=1) # Stephenson's statistic, m=5 senU(d,m1=5,m2=5,m=5,gamma=1.8) # U-statistic from Rosenbaum (2011) and # Section 19.2 of Design of Observational Studies, 2nd ed. senU(d,m1=4,m2=5,m=5,gamma=1.8) detach(schoket)
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