Update sim_gs_n() tests for overhauled milestone()

milestone() was recently overhauled to

1. Use the log-log method to calculate the `estimate`. Setting
   `test_type = "naive"` restored the previous value
1. Return a single value for `se` instead of two
1. Return the unsquared Z statistic for `z`

Merck#237

tests/testthat/test-unvalidated-sim_gs_n.R

@@ -202,7 +202,8 @@ test_that("Milestone", {
     test = milestone,
     cut = test_cutting(),
     seed = 2024,
-    ms_time = 10
+    ms_time = 10,
+    test_type = "naive"
   )
   expected <- data.frame(
     sim_id = rep(1:3, each = 3L),
@@ -217,21 +218,15 @@ test_that("Milestone", {
       0.12045851966961263, 0.11738941400903585, 0.11738941400903585,
       0.15355822418246762, 0.1519773404060174, 0.1519773404060174
     ),
-    se = I(list(
-      c(0.03693587681297664, 0.03662189834863626),
-      c(0.034952703615152854, 0.03484070894801079),
-      c(0.034952703615152854, 0.03484070894801079),
-      c(0.03614098127448581, 0.035312669921649095),
-      c(0.035432630739150366, 0.034912158581439694),
-      c(0.035432630739150366, 0.034912158581439694),
-      c(0.035815727559287504, 0.03505127094114008),
-      c(0.03540131462139614, 0.034738243333119145),
-      c(0.03540131462139614, 0.034738243333119145)
-    )),
+    se = c(
+      0.0735577751616129, 0.06979341256316365, 0.06979341256316365,
+      0.0714536511961349, 0.07034478932059006, 0.07034478932059006,
+      0.07086699850042757, 0.07013955795451529, 0.07013955795451529
+    ),
     z = c(
-      9.252619142383594, 12.078380683791904, 12.078380683791904, 5.565741269919053,
-      5.457930240636103, 5.457930240636103, 9.051772787302813, 9.054982526543846,
-      9.054982526543846
+      2.188360418259918, 2.5113130710591616, 2.5113130710591616, 1.685827353160205,
+      1.6687719892662431, 1.6687719892662431, 2.1668509663428335,
+      2.1667849760982665, 2.1667849760982665
     )
   )
   expect_equal(observed, expected)
@@ -240,7 +235,7 @@ test_that("Milestone", {
 test_that("WLR with fh(0, 0.5) test at IA1, WLR with mb(6, Inf) at IA2, and milestone test at FA", {
   ia1_test <- create_test(wlr, weight = fh(rho = 0, gamma = 0.5))
   ia2_test <- create_test(wlr, weight = mb(delay = 6, w_max = Inf))
-  fa_test <- create_test(milestone, ms_time = 10)
+  fa_test <- create_test(milestone, ms_time = 10, test_type = "naive")
 
   observed <- sim_gs_n(
    n_sim = 3,
@@ -264,20 +259,17 @@ test_that("WLR with fh(0, 0.5) test at IA1, WLR with mb(6, Inf) at IA2, and mile
       -15.500239367897708, -44.437051762182506, 0.11738941400903585,
       -12.664624037110103, -41.66249375734963, 0.1519773404060174
     ),
-    se = I(list(
-      4.081359229309616, 11.636448672437368,
-      c(0.034952703615152854, 0.03484070894801079), 4.299539499761723,
-      11.272930548434758, c(0.035432630739150366, 0.034912158581439694),
-      4.02917790229253, 11.25721499206715,
-      c(0.03540131462139614, 0.034738243333119145)
-    )),
+    se = c(
+      4.081359229309616, 11.636448672437368, 0.06979341256316365, 4.299539499761723,
+      11.272930548434758, 0.07034478932059006, 4.02917790229253, 11.25721499206715,
+      0.07013955795451529
+    ),
     z = c(
-      -4.149161171743935, -4.738039225196407, 12.078380683791904,
-      -3.605092910242299, -3.9419254444313574, 5.457930240636103,
-      -3.1432278107909206, -3.7009592325196583, 9.054982526543846
+      -4.149161171743935, -4.738039225196407, 2.5113130710591616,
+      -3.605092910242299, -3.9419254444313574, 1.6687719892662431,
+      -3.1432278107909206, -3.7009592325196583, 2.1667849760982665
     )
   )
-  class(expected$se) <- NULL # I() adds the class "AsIs"
   expect_equal(observed, expected)
 })
 
@@ -385,7 +377,7 @@ test_that("sim_gs_n() requires a test for each cutting", {
 test_that("sim_gs_n() can combine wlr(), rmst(), and milestone() tests", {
   test_cut1 <- create_test(wlr, weight = fh(rho = 0, gamma = 0))
   test_cut2 <- create_test(rmst, tau = 20)
-  test_cut3 <- create_test(milestone, ms_time = 10)
+  test_cut3 <- create_test(milestone, ms_time = 10, test_type = "naive")
 
   observed <- sim_gs_n(
     n_sim = 3,
@@ -409,20 +401,17 @@ test_that("sim_gs_n() can combine wlr(), rmst(), and milestone() tests", {
       -26.84871111584948, 2.3996923551365086, 0.11738941400903585,
       -23.063020152157016, 2.4481174776559786, 0.1519773404060174
     ),
-    se = I(list(
-      7.521418120132856, 0.7147367073498636,
-      c(0.034952703615152854, 0.03484070894801079), 7.7214837796562,
-      0.7282783320819864, c(0.035432630739150366, 0.034912158581439694),
-      7.498065002594769, 0.7416397937499536,
-      c(0.03540131462139614, 0.034738243333119145)
-    )),
+    se = c(
+      7.521418120132856, 0.7147367073498636, 0.06979341256316365, 7.7214837796562,
+      0.7282783320819864, 0.07034478932059006, 7.498065002594769,
+      0.7416397937499536, 0.07013955795451529
+    ),
     z = c(
-      -3.7486049782713247, 3.991862864282945, 12.078380683791904,
-      -3.4771440155825752, 3.2950209410683957, 5.457930240636103,
-      -3.075862925191481, 3.3009521580248022, 9.054982526543846
+      -3.7486049782713247, 3.991862864282945, 2.5113130710591616,
+      -3.4771440155825752, 3.2950209410683957, 1.6687719892662431,
+      -3.075862925191481, 3.3009521580248022, 2.1667849760982665
     )
   )
-  class(expected$se) <- NULL # I() adds the class "AsIs"
   expect_equal(observed, expected)
 })