library(survival) test_that("rmdiff: unstratified test of rmst difference", { df1 <- rmdiff(veteran, treat="trt", time="time", event="status", milestone=90) # the following values are obtained from SAS PROC LIFETEST: # proc lifetest data=veteran rmst(tau=90); # time time*status(0); # strata trt; # run; rmst1 = 62.75618 rmst2 = 56.45116 stderr1 = 4.0321 stderr2 = 3.9790 rmstdiffchisq = 1.2388 pvalue = 0.2657 expect_equal(c(round(df1$rmst1, 5), round(df1$rmst2, 5), round(sqrt(df1$vrmst1), 4), round(sqrt(df1$vrmst2), 4), round(df1$rmstDiffZ^2, 4), round(2*min(df1$rmstDiffPValue, 1-df1$rmstDiffPValue), 4)), c(rmst1, rmst2, stderr1, stderr2, rmstdiffchisq, pvalue)) }) test_that("rmdiff: stratified test of rmst difference", { df1 <- rmdiff(veteran, stratum="celltype", treat="trt", time="time", event="status", milestone=90) # Of note, the stratified results are different from SAS PROC LIFETEST: # proc lifetest data=veteran rmst(tau=90); # time time*status(0); # strata celltype / group = trt; # run; # This is because SAS adds up the rmst diffs and variances across strata, # while we use the number of subjects as the weight for each stratum. # Our approach yields a more meaningful rmst diff across strata. # To reproduce our results, we combine the stratum-specific information # from SAS PROC LIFETEST using the sample size weights across strata n = c(15, 20, 30, 18, 9, 18, 15, 12) rmst = c(68.55152, 65.95, 50.86667, 45.16667, 56.44444, 51.91667, 84.8, 64.25) stderr = c(8.3317, 7.5975, 5.7978, 7.8103, 12.7986, 6.9569, 5.0237, 8.0847) a = c(1, 3, 5, 7) b = c(2, 4, 6, 8) ns = n[a] + n[b] rmstDiffs = rmst[a] - rmst[b] vrmstDiffs = stderr[a]^2 + stderr[b]^2 w = ns/sum(ns) rmstDiff = sum(w*rmstDiffs) vrmstDiff = sum(w*w*vrmstDiffs) rmstDiffZ = rmstDiff/sqrt(vrmstDiff) expect_equal(c(round(df1$rmstDiff, 4), round(sqrt(df1$vrmstDiff), 3), round(df1$rmstDiffZ, 3)), c(round(rmstDiff, 4), round(sqrt(vrmstDiff), 3), round(rmstDiffZ, 3))) })