context("ve") # Data data(data_temp) tolerance <- 0.001 set.seed(1) # ve logisticFit <- glm(disease_any ~ nAb1, data = data_temp, family = binomial()) logisticVE <- ve(logisticFit, data_temp, nboot = 500) coxFit <- coxph(Surv(time_event, disease_any) ~ nAb1, data = data_temp) coxVE <- ve(coxFit, data_temp, nboot = 500) test_that("ve", {expect_equal(logisticVE$VE, 0.2025612, tolerance = tolerance) expect_equal(logisticVE$CI$LB, -1.789451, tolerance = tolerance) expect_equal(logisticVE$CI$UB, 2.667624, tolerance = tolerance) expect_equal(coxVE$VE, 0.1198574, tolerance = tolerance) expect_equal(coxVE$CI$LB, -1.764782, tolerance = tolerance) expect_equal(coxVE$CI$UB, 2.070932, tolerance = tolerance) }) # glmParametricSampling set.seed(1) Data.vaccinated <- filter(data_temp, vaccine == 1) Data.control <- filter(data_temp, vaccine == 0) logisticFit <- glm(disease_any ~ nAb1, data = data_temp, family = binomial()) efficacySet <- glmParametricSampling(logisticFit, nboot = 500, Data.vaccinated, Data.control) CI <- lapply(EfficacyCI(efficacySet),"*", 100) test_that("glmParametricSampling", {expect_equal(CI$mean, 0.2468702, tolerance = tolerance) expect_equal(CI$median, 0.08851155, tolerance = tolerance) expect_equal(CI$CILow, -1.789451, tolerance = tolerance) expect_equal(CI$CIHigh, 2.667624, tolerance = tolerance) expect_equal(length(efficacySet), 500) }) # coxphParametricSampling set.seed(1) Data.vaccinated <- filter(data_temp, vaccine == 1) Data.control <- filter(data_temp, vaccine == 0) coxFit <- coxph(Surv(time_event, disease_any) ~ nAb1, data = data_temp) efficacySet <- coxphParametricSampling(coxFit, nboot = 500, Data.vaccinated, Data.control) CI <- lapply(EfficacyCI(efficacySet),"*", 100) test_that("coxphParametricSampling", {expect_equal(CI$mean, 0.07762033, tolerance = tolerance) expect_equal(CI$median, 0.01538626, tolerance = tolerance) expect_equal(CI$CILow, -1.934508, tolerance = tolerance) expect_equal(CI$CIHigh, 2.330776, tolerance = tolerance) expect_equal(length(efficacySet), 500) })