Getting started with gsDesignTune

This vignette demonstrates dependency-aware grid search over group sequential designs using gsDesignTune.

library(gsDesign)
library(gsDesignTune)

Basic designs with gsDesignTune()

gsDesignTune() wraps gsDesign::gsDesign() for tuning basic group sequential designs.

job <- gsDesignTune(
  k = 3,
  test.type = 2,
  alpha = 0.025,
  beta = 0.10,
  timing = tune_values(list(c(0.33, 0.67, 1), c(0.5, 0.75, 1))),
  upper = SpendingFamily$new(
    SpendingSpec$new(sfLDOF, par = tune_fixed(0)),
    SpendingSpec$new(sfHSD, par = tune_seq(-4, 4, length_out = 3))
  )
)

job$run(strategy = "grid", parallel = FALSE)
res <- job$results()
head(res)
#>   upper_setting       timing upper_fun upper_par config_id status error_message
#> 1  function.... 0.33, 0.....    sfLDOF         0         1     ok          <NA>
#> 2  function.... 0.5, 0.75, 1    sfLDOF         0         2     ok          <NA>
#> 3  function.... 0.33, 0.....     sfHSD        -4         3     ok          <NA>
#> 4  function.... 0.5, 0.75, 1     sfHSD        -4         4     ok          <NA>
#> 5  function.... 0.33, 0.....     sfHSD         0         5     ok          <NA>
#> 6  function.... 0.5, 0.75, 1     sfHSD         0         6     ok          <NA>
#>   warnings                        cache_key design_rds    call_args k test.type
#> 1     <NA> 2a02c8567abd8c7ed8822d4940901162       <NA> 3, 2, 0..... 3         2
#> 2     <NA> a9c397fd736584ab0260cd83ceaeba5e       <NA> 3, 2, 0..... 3         2
#> 3     <NA> e7580124f44920908f1d2135656f0ab1       <NA> 3, 2, 0..... 3         2
#> 4     <NA> f4451293194cac770d2ec4b1ee82d8ac       <NA> 3, 2, 0..... 3         2
#> 5     <NA> 6f382b0daa41a36567f87d7907c8596c       <NA> 3, 2, 0..... 3         2
#> 6     <NA> 6e4d4614c851b42a6d3f1d663bfab362       <NA> 3, 2, 0..... 3         2
#>   alpha beta          n_I final_n_I      upper_z      lower_z      upper_p
#> 1 0.025  0.1 0.333977....  1.012053 3.7307, .... -3.7307,.... 1e-04, 0....
#> 2 0.025  0.1 0.509137....  1.018275 2.9626, .... -2.9626,.... 0.0015, ....
#> 3 0.025  0.1 0.335043....  1.015284 3.0162, .... -3.0162,.... 0.0013, ....
#> 4 0.025  0.1 0.509227....  1.018456 2.75, 2..... -2.75, -.... 0.003, 0....
#> 5 0.025  0.1 0.365230....  1.106759 2.3977, .... -2.3977,.... 0.0082, ....
#> 6 0.025  0.1 0.555438....  1.110878 2.2414, .... -2.2414,.... 0.0125, ....
#>        lower_p power           en                               upper_name
#> 1 1e-04, 0....   0.9 1.007862.... Lan-DeMets O'Brien-Fleming approximation
#> 2 0.0015, ....   0.9 1.012586.... Lan-DeMets O'Brien-Fleming approximation
#> 3 0.0013, ....   0.9 1.010154....                        Hwang-Shih-DeCani
#> 4 0.003, 0....   0.9 1.012404....                        Hwang-Shih-DeCani
#> 5 0.0082, ....   0.9 1.088314....                        Hwang-Shih-DeCani
#> 6 0.0125, ....   0.9 1.093520....                        Hwang-Shih-DeCani
#>                                 lower_name bound_summary  final_n    max_n
#> 1 Lan-DeMets O'Brien-Fleming approximation  c("IA 1:.... 1.012053 1.012053
#> 2 Lan-DeMets O'Brien-Fleming approximation  c("IA 1:.... 1.018275 1.018275
#> 3                        Hwang-Shih-DeCani  c("IA 1:.... 1.015284 1.015284
#> 4                        Hwang-Shih-DeCani  c("IA 1:.... 1.018456 1.018456
#> 5                        Hwang-Shih-DeCani  c("IA 1:.... 1.106759 1.106759
#> 6                        Hwang-Shih-DeCani  c("IA 1:.... 1.110878 1.110878
#>   upper_z1 lower_z1
#> 1   3.7307  -3.7307
#> 2   2.9626  -2.9626
#> 3   3.0162  -3.0162
#> 4   2.7500  -2.7500
#> 5   2.3977  -2.3977
#> 6   2.2414  -2.2414

Ranking and filtering

best <- job$best("final_n", direction = "min")
head(best, 10)
#>   upper_setting       timing upper_fun upper_par config_id status error_message
#> 1  function.... 0.33, 0.....    sfLDOF         0         1     ok          <NA>
#> 3  function.... 0.33, 0.....     sfHSD        -4         3     ok          <NA>
#> 2  function.... 0.5, 0.75, 1    sfLDOF         0         2     ok          <NA>
#> 4  function.... 0.5, 0.75, 1     sfHSD        -4         4     ok          <NA>
#> 5  function.... 0.33, 0.....     sfHSD         0         5     ok          <NA>
#> 6  function.... 0.5, 0.75, 1     sfHSD         0         6     ok          <NA>
#> 8  function.... 0.5, 0.75, 1     sfHSD         4         8     ok          <NA>
#> 7  function.... 0.33, 0.....     sfHSD         4         7     ok          <NA>
#>   warnings                        cache_key design_rds    call_args k test.type
#> 1     <NA> 2a02c8567abd8c7ed8822d4940901162       <NA> 3, 2, 0..... 3         2
#> 3     <NA> e7580124f44920908f1d2135656f0ab1       <NA> 3, 2, 0..... 3         2
#> 2     <NA> a9c397fd736584ab0260cd83ceaeba5e       <NA> 3, 2, 0..... 3         2
#> 4     <NA> f4451293194cac770d2ec4b1ee82d8ac       <NA> 3, 2, 0..... 3         2
#> 5     <NA> 6f382b0daa41a36567f87d7907c8596c       <NA> 3, 2, 0..... 3         2
#> 6     <NA> 6e4d4614c851b42a6d3f1d663bfab362       <NA> 3, 2, 0..... 3         2
#> 8     <NA> e976042d0e8e2b27179997359fee7ab1       <NA> 3, 2, 0..... 3         2
#> 7     <NA> 92b57bf5daf6e42077127fba752ee177       <NA> 3, 2, 0..... 3         2
#>   alpha beta          n_I final_n_I      upper_z      lower_z      upper_p
#> 1 0.025  0.1 0.333977....  1.012053 3.7307, .... -3.7307,.... 1e-04, 0....
#> 3 0.025  0.1 0.335043....  1.015284 3.0162, .... -3.0162,.... 0.0013, ....
#> 2 0.025  0.1 0.509137....  1.018275 2.9626, .... -2.9626,.... 0.0015, ....
#> 4 0.025  0.1 0.509227....  1.018456 2.75, 2..... -2.75, -.... 0.003, 0....
#> 5 0.025  0.1 0.365230....  1.106759 2.3977, .... -2.3977,.... 0.0082, ....
#> 6 0.025  0.1 0.555438....  1.110878 2.2414, .... -2.2414,.... 0.0125, ....
#> 8 0.025  0.1 0.675309....  1.350620 2.0137, .... -2.0137,.... 0.022, 0....
#> 7 0.025  0.1 0.451412....  1.367918 2.0822, .... -2.0822,.... 0.0187, ....
#>        lower_p power           en                               upper_name
#> 1 1e-04, 0....   0.9 1.007862.... Lan-DeMets O'Brien-Fleming approximation
#> 3 0.0013, ....   0.9 1.010154....                        Hwang-Shih-DeCani
#> 2 0.0015, ....   0.9 1.012586.... Lan-DeMets O'Brien-Fleming approximation
#> 4 0.003, 0....   0.9 1.012404....                        Hwang-Shih-DeCani
#> 5 0.0082, ....   0.9 1.088314....                        Hwang-Shih-DeCani
#> 6 0.0125, ....   0.9 1.093520....                        Hwang-Shih-DeCani
#> 8 0.022, 0....   0.9 1.319407....                        Hwang-Shih-DeCani
#> 7 0.0187, ....   0.9 1.329142....                        Hwang-Shih-DeCani
#>                                 lower_name bound_summary  final_n    max_n
#> 1 Lan-DeMets O'Brien-Fleming approximation  c("IA 1:.... 1.012053 1.012053
#> 3                        Hwang-Shih-DeCani  c("IA 1:.... 1.015284 1.015284
#> 2 Lan-DeMets O'Brien-Fleming approximation  c("IA 1:.... 1.018275 1.018275
#> 4                        Hwang-Shih-DeCani  c("IA 1:.... 1.018456 1.018456
#> 5                        Hwang-Shih-DeCani  c("IA 1:.... 1.106759 1.106759
#> 6                        Hwang-Shih-DeCani  c("IA 1:.... 1.110878 1.110878
#> 8                        Hwang-Shih-DeCani  c("IA 1:.... 1.350620 1.350620
#> 7                        Hwang-Shih-DeCani  c("IA 1:.... 1.367918 1.367918
#>   upper_z1 lower_z1
#> 1   3.7307  -3.7307
#> 3   3.0162  -3.0162
#> 2   2.9626  -2.9626
#> 4   2.7500  -2.7500
#> 5   2.3977  -2.3977
#> 6   2.2414  -2.2414
#> 8   2.0137  -2.0137
#> 7   2.0822  -2.0822

Plot

job$plot(metric = "final_n", x = "upper_par", color = "upper_fun")

Survival designs with gsSurvTune()

gsSurvTune() wraps gsDesign::gsSurv() for tuning time-to-event designs.

job_surv <- gsSurvTune(
  k = 3,
  test.type = 4,
  alpha = 0.025,
  beta = 0.10,
  timing = tune_values(list(c(0.33, 0.67, 1), c(0.5, 0.75, 1))),
  hr = tune_seq(0.60, 0.75, length_out = 3),
  upper = SpendingFamily$new(
    SpendingSpec$new(sfLDOF, par = tune_fixed(0)),
    SpendingSpec$new(sfHSD, par = tune_seq(-4, 4, length_out = 3))
  ),
  lower = SpendingSpec$new(sfLDOF, par = tune_fixed(0)),
  lambdaC = log(2) / 6,
  eta = 0.01,
  gamma = c(2.5, 5, 7.5, 10),
  R = c(2, 2, 2, 6),
  T = 18,
  minfup = 6,
  ratio = 1
)

job_surv$run(strategy = "grid", parallel = FALSE)
res_surv <- job_surv$results()
head(res_surv)
#>   upper_setting lower_setting       timing    hr upper_fun upper_par lower_fun
#> 1  function....  function.... 0.33, 0..... 0.600    sfLDOF         0    sfLDOF
#> 2  function....  function.... 0.33, 0..... 0.675    sfLDOF         0    sfLDOF
#> 3  function....  function.... 0.33, 0..... 0.750    sfLDOF         0    sfLDOF
#> 4  function....  function.... 0.5, 0.75, 1 0.600    sfLDOF         0    sfLDOF
#> 5  function....  function.... 0.5, 0.75, 1 0.675    sfLDOF         0    sfLDOF
#> 6  function....  function.... 0.5, 0.75, 1 0.750    sfLDOF         0    sfLDOF
#>   lower_par config_id status error_message warnings
#> 1         0         1     ok          <NA>     <NA>
#> 2         0         2     ok          <NA>     <NA>
#> 3         0         3     ok          <NA>     <NA>
#> 4         0         4     ok          <NA>     <NA>
#> 5         0         5     ok          <NA>     <NA>
#> 6         0         6     ok          <NA>     <NA>
#>                          cache_key design_rds    call_args k test.type alpha
#> 1 5e919de024dfb2788159f8d1d5b4b299       <NA> 3, 4, 0..... 3         4 0.025
#> 2 5b94658197250fa81f222cd42981cee2       <NA> 3, 4, 0..... 3         4 0.025
#> 3 8585e7574c0938da43ca29335694d0ac       <NA> 3, 4, 0..... 3         4 0.025
#> 4 04219b7dccfa06033ebf8e07528d56c3       <NA> 3, 4, 0..... 3         4 0.025
#> 5 bc6aa57478df46ab3cc6c0a819c2fa26       <NA> 3, 4, 0..... 3         4 0.025
#> 6 9bc0588d4895878555a71a64d4fa7104       <NA> 3, 4, 0..... 3         4 0.025
#>   beta          n_I final_n_I      upper_z      lower_z      upper_p
#> 1  0.1 56.24907....  170.4517 3.7307, .... -0.719, .... 1e-04, 0....
#> 2  0.1 95.02760....  287.9624 3.7307, .... -0.719, .... 1e-04, 0....
#> 3  0.1 177.4604....  537.7591 3.7307, .... -0.719, .... 1e-04, 0....
#> 4  0.1 87.06008....  174.1202 2.9626, .... 0.3316, .... 0.0015, ....
#> 5  0.1 147.0799....  294.1598 2.9626, .... 0.3316, .... 0.0015, ....
#> 6  0.1 274.6662....  549.3326 2.9626, .... 0.3316, .... 0.0015, ....
#>        lower_p power           en                               upper_name
#> 1 0.7639, ....   0.9 108.7925.... Lan-DeMets O'Brien-Fleming approximation
#> 2 0.7639, ....   0.9 183.7948.... Lan-DeMets O'Brien-Fleming approximation
#> 3 0.7639, ....   0.9 343.2300.... Lan-DeMets O'Brien-Fleming approximation
#> 4 0.3701, ....   0.9 106.7777.... Lan-DeMets O'Brien-Fleming approximation
#> 5 0.3701, ....   0.9 180.3910.... Lan-DeMets O'Brien-Fleming approximation
#> 6 0.3701, ....   0.9 336.8735.... Lan-DeMets O'Brien-Fleming approximation
#>                                 lower_name bound_summary final_events
#> 1 Lan-DeMets O'Brien-Fleming approximation  c("IA 1:....     170.4517
#> 2 Lan-DeMets O'Brien-Fleming approximation  c("IA 1:....     287.9624
#> 3 Lan-DeMets O'Brien-Fleming approximation  c("IA 1:....     537.7591
#> 4 Lan-DeMets O'Brien-Fleming approximation  c("IA 1:....     174.1202
#> 5 Lan-DeMets O'Brien-Fleming approximation  c("IA 1:....     294.1598
#> 6 Lan-DeMets O'Brien-Fleming approximation  c("IA 1:....     549.3326
#>   max_events      n_total final_n_total analysis_time upper_z1 lower_z1
#> 1   170.4517 216, 296....           296  9.582183....   3.7307  -0.7190
#> 2   287.9624 352, 482....           482  9.554795....   3.7307  -0.7190
#> 3   537.7591 634, 874....           874  9.524268....   3.7307  -0.7190
#> 4   174.1202 284, 302....           302  11.48078....   2.9626   0.3316
#> 5   294.1598 462, 492....           492  11.45171....   2.9626   0.3316
#> 6   549.3326 834, 892....           892  11.41939....   2.9626   0.3316

Calendar-timed analyses with gsSurvCalendarTune()

gsSurvCalendarTune() is similar to gsSurvTune(), but you specify planned calendar times of analyses via calendarTime instead of information timing.

job_cal <- gsSurvCalendarTune(
  test.type = 4,
  alpha = 0.025,
  beta = 0.10,
  calendarTime = tune_values(list(c(12, 24, 36), c(9, 18, 27))),
  spending = tune_choice("information", "calendar"),
  hr = tune_seq(0.60, 0.75, length_out = 3),
  upper = SpendingFamily$new(
    SpendingSpec$new(sfLDOF, par = tune_fixed(0)),
    SpendingSpec$new(sfHSD, par = tune_seq(-4, 4, length_out = 3))
  ),
  lower = SpendingSpec$new(sfLDOF, par = tune_fixed(0)),
  lambdaC = log(2) / 6,
  eta = 0.01,
  gamma = c(2.5, 5, 7.5, 10),
  R = c(2, 2, 2, 6),
  minfup = 18,
  ratio = 1
)

job_cal$run(strategy = "grid", parallel = FALSE)
res_cal <- job_cal$results()
head(res_cal)
#>   upper_setting lower_setting calendarTime    spending    hr upper_fun
#> 1  function....  function....   12, 24, 36 information 0.600    sfLDOF
#> 2  function....  function....   12, 24, 36 information 0.675    sfLDOF
#> 3  function....  function....   12, 24, 36 information 0.750    sfLDOF
#> 4  function....  function....   12, 24, 36    calendar 0.600    sfLDOF
#> 5  function....  function....   12, 24, 36    calendar 0.675    sfLDOF
#> 6  function....  function....   12, 24, 36    calendar 0.750    sfLDOF
#>   upper_par lower_fun lower_par config_id status error_message warnings
#> 1         0    sfLDOF         0         1     ok          <NA>     <NA>
#> 2         0    sfLDOF         0         2     ok          <NA>     <NA>
#> 3         0    sfLDOF         0         3     ok          <NA>     <NA>
#> 4         0    sfLDOF         0         4     ok          <NA>     <NA>
#> 5         0    sfLDOF         0         5     ok          <NA>     <NA>
#> 6         0    sfLDOF         0         6     ok          <NA>     <NA>
#>                          cache_key design_rds    call_args k test.type alpha
#> 1 04ceef8a37465aeb1941067d8d34f1f8       <NA> 4, 0.025.... 3         4 0.025
#> 2 5f4af16b3eda4ea07df499f2cb2505d6       <NA> 4, 0.025.... 3         4 0.025
#> 3 5fa33bd69dc3aa025743081af7ada6f1       <NA> 4, 0.025.... 3         4 0.025
#> 4 7e58415c5a24209eb2796b503e708c55       <NA> 4, 0.025.... 3         4 0.025
#> 5 24143227e497c5aaffb5eda95e125273       <NA> 4, 0.025.... 3         4 0.025
#> 6 87f2aed2d7ee96bba1352882f58e9c44       <NA> 4, 0.025.... 3         4 0.025
#>   beta       timing          n_I final_n_I      upper_z      lower_z
#> 1  0.1 0.234953.... 40.57744....  172.7038 4.4783, .... -1.5645,....
#> 2  0.1 0.239261.... 70.11502....  293.0477 4.4352, .... -1.5165,....
#> 3  0.1 0.243794.... 133.8101....  548.8641 4.3911, .... -1.467, ....
#> 4  0.1 0.234953.... 38.77267....  165.0225 3.7103, .... -1.0234,....
#> 5  0.1 0.239261.... 66.81872....  279.2707 3.7103, .... -1.0102,....
#> 6  0.1 0.243794.... 127.1925....  521.7201 3.7103, .... -0.9963,....
#>        upper_p      lower_p power           en
#> 1 0, 0.011.... 0.9412, ....   0.9 132.7343....
#> 2 0, 0.012.... 0.9353, ....   0.9 226.1005....
#> 3 0, 0.012.... 0.9288, ....   0.9 424.9595....
#> 4 1e-04, 0.... 0.8469, ....   0.9 119.9537....
#> 5 1e-04, 0.... 0.8438, ....   0.9 204.1470....
#> 6 1e-04, 0.... 0.8404, ....   0.9 383.4977....
#>                                 upper_name
#> 1 Lan-DeMets O'Brien-Fleming approximation
#> 2 Lan-DeMets O'Brien-Fleming approximation
#> 3 Lan-DeMets O'Brien-Fleming approximation
#> 4 Lan-DeMets O'Brien-Fleming approximation
#> 5 Lan-DeMets O'Brien-Fleming approximation
#> 6 Lan-DeMets O'Brien-Fleming approximation
#>                                 lower_name bound_summary final_events
#> 1 Lan-DeMets O'Brien-Fleming approximation  c("IA 1:....     172.7038
#> 2 Lan-DeMets O'Brien-Fleming approximation  c("IA 1:....     293.0477
#> 3 Lan-DeMets O'Brien-Fleming approximation  c("IA 1:....     548.8641
#> 4 Lan-DeMets O'Brien-Fleming approximation  c("IA 1:....     165.0225
#> 5 Lan-DeMets O'Brien-Fleming approximation  c("IA 1:....     279.2707
#> 6 Lan-DeMets O'Brien-Fleming approximation  c("IA 1:....     521.7201
#>   max_events      n_total final_n_total analysis_time upper_z1 lower_z1
#> 1   172.7038 128, 212....           212    12, 24, 36   4.4783  -1.5645
#> 2   293.0477 212, 354....           354    12, 24, 36   4.4352  -1.5165
#> 3   548.8641 390, 650....           650    12, 24, 36   4.3911  -1.4670
#> 4   165.0225 122, 204....           204    12, 24, 36   3.7103  -1.0234
#> 5   279.2707 202, 336....           336    12, 24, 36   3.7103  -1.0102
#> 6   521.7201 372, 618....           618    12, 24, 36   3.7103  -0.9963

Multi-scenario exploration

best_surv <- job_surv$best("final_events", direction = "min")
head(best_surv, 10)
#>    upper_setting lower_setting       timing    hr upper_fun upper_par lower_fun
#> 1   function....  function.... 0.33, 0..... 0.600    sfLDOF         0    sfLDOF
#> 7   function....  function.... 0.33, 0..... 0.600     sfHSD        -4    sfLDOF
#> 4   function....  function.... 0.5, 0.75, 1 0.600    sfLDOF         0    sfLDOF
#> 10  function....  function.... 0.5, 0.75, 1 0.600     sfHSD        -4    sfLDOF
#> 13  function....  function.... 0.33, 0..... 0.600     sfHSD         0    sfLDOF
#> 16  function....  function.... 0.5, 0.75, 1 0.600     sfHSD         0    sfLDOF
#> 22  function....  function.... 0.5, 0.75, 1 0.600     sfHSD         4    sfLDOF
#> 19  function....  function.... 0.33, 0..... 0.600     sfHSD         4    sfLDOF
#> 2   function....  function.... 0.33, 0..... 0.675    sfLDOF         0    sfLDOF
#> 8   function....  function.... 0.33, 0..... 0.675     sfHSD        -4    sfLDOF
#>    lower_par config_id status error_message warnings
#> 1          0         1     ok          <NA>     <NA>
#> 7          0         7     ok          <NA>     <NA>
#> 4          0         4     ok          <NA>     <NA>
#> 10         0        10     ok          <NA>     <NA>
#> 13         0        13     ok          <NA>     <NA>
#> 16         0        16     ok          <NA>     <NA>
#> 22         0        22     ok          <NA>     <NA>
#> 19         0        19     ok          <NA>     <NA>
#> 2          0         2     ok          <NA>     <NA>
#> 8          0         8     ok          <NA>     <NA>
#>                           cache_key design_rds    call_args k test.type alpha
#> 1  5e919de024dfb2788159f8d1d5b4b299       <NA> 3, 4, 0..... 3         4 0.025
#> 7  d7889d921ed14ae812e3ed0d2cf3b501       <NA> 3, 4, 0..... 3         4 0.025
#> 4  04219b7dccfa06033ebf8e07528d56c3       <NA> 3, 4, 0..... 3         4 0.025
#> 10 e7314dba5cf8f0e027bfce428760e159       <NA> 3, 4, 0..... 3         4 0.025
#> 13 089bc8d1fee469d6313ea8d01c3918e3       <NA> 3, 4, 0..... 3         4 0.025
#> 16 94a2881403a82b791e8152b97005a523       <NA> 3, 4, 0..... 3         4 0.025
#> 22 4189ddae3301e4cacf176e70e82f2f1c       <NA> 3, 4, 0..... 3         4 0.025
#> 19 6b85b2a65593a3ba4370d310e1ffac77       <NA> 3, 4, 0..... 3         4 0.025
#> 2  5b94658197250fa81f222cd42981cee2       <NA> 3, 4, 0..... 3         4 0.025
#> 8  25cc8f2d4d112a17eef1b8d0b6ab8c66       <NA> 3, 4, 0..... 3         4 0.025
#>    beta          n_I final_n_I      upper_z      lower_z      upper_p
#> 1   0.1 56.24907....  170.4517 3.7307, .... -0.719, .... 1e-04, 0....
#> 7   0.1 56.42214....  170.9762 3.0162, .... -0.7161,.... 0.0013, ....
#> 4   0.1 87.06008....  174.1202 2.9626, .... 0.3316, .... 0.0015, ....
#> 10  0.1 87.11271....  174.2254 2.75, 2..... 0.3323, .... 0.003, 0....
#> 13  0.1 61.09307....  185.1305 2.3977, .... -0.6382,.... 0.0082, ....
#> 16  0.1 93.90369....  187.8074 2.2414, .... 0.4235, .... 0.0125, ....
#> 22  0.1 111.9806....  223.9612 2.0137, .... 0.6515, .... 0.022, 0....
#> 19  0.1 74.60758....  226.0836 2.0822, .... -0.4282,.... 0.0187, ....
#> 2   0.1 95.02760....  287.9624 3.7307, .... -0.719, .... 1e-04, 0....
#> 8   0.1 95.31999....  288.8485 3.0162, .... -0.7161,.... 0.0013, ....
#>         lower_p     power           en                               upper_name
#> 1  0.7639, .... 0.9000000 108.7925.... Lan-DeMets O'Brien-Fleming approximation
#> 7  0.763, 0.... 0.9000000 108.9419....                        Hwang-Shih-DeCani
#> 4  0.3701, .... 0.9000000 106.7777.... Lan-DeMets O'Brien-Fleming approximation
#> 10 0.3698, .... 0.9000000 106.7939....                        Hwang-Shih-DeCani
#> 13 0.7383, .... 0.9000000 113.8644....                        Hwang-Shih-DeCani
#> 16 0.336, 0.... 0.9000000 111.8445....                        Hwang-Shih-DeCani
#> 22 0.2574, .... 0.9000000 126.5371....                        Hwang-Shih-DeCani
#> 19 0.6658, .... 0.8999999 128.5347....                        Hwang-Shih-DeCani
#> 2  0.7639, .... 0.9000000 183.7948.... Lan-DeMets O'Brien-Fleming approximation
#> 8  0.763, 0.... 0.9000000 184.0472....                        Hwang-Shih-DeCani
#>                                  lower_name bound_summary final_events
#> 1  Lan-DeMets O'Brien-Fleming approximation  c("IA 1:....     170.4517
#> 7  Lan-DeMets O'Brien-Fleming approximation  c("IA 1:....     170.9762
#> 4  Lan-DeMets O'Brien-Fleming approximation  c("IA 1:....     174.1202
#> 10 Lan-DeMets O'Brien-Fleming approximation  c("IA 1:....     174.2254
#> 13 Lan-DeMets O'Brien-Fleming approximation  c("IA 1:....     185.1305
#> 16 Lan-DeMets O'Brien-Fleming approximation  c("IA 1:....     187.8074
#> 22 Lan-DeMets O'Brien-Fleming approximation  c("IA 1:....     223.9612
#> 19 Lan-DeMets O'Brien-Fleming approximation  c("IA 1:....     226.0836
#> 2  Lan-DeMets O'Brien-Fleming approximation  c("IA 1:....     287.9624
#> 8  Lan-DeMets O'Brien-Fleming approximation  c("IA 1:....     288.8485
#>    max_events      n_total final_n_total analysis_time upper_z1 lower_z1
#> 1    170.4517 216, 296....           296  9.582183....   3.7307  -0.7190
#> 7    170.9762 218, 296....           296  9.582183....   3.0162  -0.7161
#> 4    174.1202 284, 302....           302  11.48078....   2.9626   0.3316
#> 10   174.2254 284, 302....           302  11.48078....   2.7500   0.3323
#> 13   185.1305 234, 320....           320  9.582183....   2.3977  -0.6382
#> 16   187.8074 306, 326....           326  11.48078....   2.2414   0.4235
#> 22   223.9612 366, 388....           388  11.48078....   2.0137   0.6515
#> 19   226.0836 286, 392....           392  9.582183....   2.0822  -0.4282
#> 2    287.9624 352, 482....           482  9.554795....   3.7307  -0.7190
#> 8    288.8485 352, 484....           484  9.554795....   3.0162  -0.7161
job_surv$plot(metric = "final_events", x = "hr", color = "upper_fun")

Export a report

report_path <- tempfile(fileext = ".html")
job_surv$report(report_path)
report_path
#> [1] "/var/folders/f2/bgpmhwqs3k9g88gtrfxjf4rh0000gn/T//RtmpFH758y/file24703065ee2d.html"