This data set contains 2,685 patients in the Breast International Group (BIG) 1-98 randomized clinical trial. The BIG 1-98 is a Phase III clinical trial of 8,010 post menopausal women with hormone-receptor-positive early invasive breast cancer who were randomly assigned adjuvant therapy of letrozole or tamoxifen. Patterns of treatment effects for varying levels of the biomarker Ki-67 labeling index, a measure of cell proliferation, were analyzed using STEPP. The STEPP analysis showed that letrozole was more effective than tamoxifen for patients with tumors expressing the highest levels of the Ki-67 labeling index. The two treatment arms are letrozole and tamoxifen.
There are four columns of numeric values: trt (treatment group), time (time to event), event (competing event types), and ki67 (continuous measurement of biomarker Ki-67).
The Breast International Group (BIG) 1-98 Steering Committee and the International Breast Cancer Study Group (IBCSG) are acknowledged for permission to use the data from the BIG 1-98 trial.
Lazar A, Cole B, Bonetti M, Gelber R (2010), "Evaluation of treatment-effect heterogeneity using biomarkers measured on a continuous scale: subpopulation treatment effect pattern plot." J Clin Oncol, 28(29), 4539-44.
Viale G et al (2008), "Prognostic and predictive value of cnetrally reviewed Ki-67 labeling index in postmenopausal women with endocrine-responsive breast cancer: results from Breast International Group Trial 1-98 comparing adjuvant tamoxifen and letrozole." J Clin Oncol, 28(34), 5569-75.
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data(bigCI) rxgroup <- bigCI$trt time <- bigCI$time evt <- bigCI$event cov <- bigCI$ki67 # analyze using Cumulative Incidence method with # sliding window size of 150 patients and a maximum of 50 patients in common # swin <- new("stwin", type="sliding", r1=50, r2=150) # create a sliding window subp <- new("stsubpop") # create subpopulation object subp <- generate(subp, win=swin, covariate=cov) # generate the subpopulations summary(subp) # summary of the subpopulations # create a stepp model using Cumulative Incidences to analyze the data # smodel <- new("stmodelCI", coltrt=rxgroup, trts=c(1,2), coltime=time, coltype=evt, timePoint=4) statCI <- new("steppes") # create a test object based on subpopulation and window statCI <- estimate(statCI, subp, smodel) # estimate the subpo10ulation results # Warning: In this example, the permutations have been set to 0 to allow the function # to finish in a short amount of time. IT IS RECOMMEND TO USE AT LEAST 2500 PERMUTATIONS TO # PROVIDE STABLE RESULTS. statCI <- test(statCI, nperm=0) # permutation test with 0 iterations print(statCI) # print the estimates and test statistics plot(statCI, ncex=0.65, legendy=30, pline=-15.5, color=c("blue","gold"), pointwise=FALSE, xlabel="Median Ki-67 LI in Subpopulation (% immunoreactivity)", ylabel="4-year Cumulative Incidence", tlegend=c("Taxmoxifen", "Letrozole"), nlas=3)
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