Getting started with 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()
job$table(n = 6)

Config ID	Upper bound	Upper parameter	Timing	Final N	Power	Upper Z (IA1)	Lower Z (IA1)
1	sfLDOF	0	0.33, 0.67, 1	1.01	0.9	3.73	-3.73
2	sfLDOF	0	0.5, 0.75, 1	1.02	0.9	2.96	-2.96
3	sfHSD	-4	0.33, 0.67, 1	1.02	0.9	3.02	-3.02
4	sfHSD	-4	0.5, 0.75, 1	1.02	0.9	2.75	-2.75
5	sfHSD	0	0.33, 0.67, 1	1.11	0.9	2.4	-2.4
6	sfHSD	0	0.5, 0.75, 1	1.11	0.9	2.24	-2.24

Ranking and filtering

best <- job$best("final_n", direction = "min")
job$table(best, n = 10)

Config ID	Upper bound	Upper parameter	Timing	Final N	Power	Upper Z (IA1)	Lower Z (IA1)
1	sfLDOF	0	0.33, 0.67, 1	1.01	0.9	3.73	-3.73
3	sfHSD	-4	0.33, 0.67, 1	1.02	0.9	3.02	-3.02
2	sfLDOF	0	0.5, 0.75, 1	1.02	0.9	2.96	-2.96
4	sfHSD	-4	0.5, 0.75, 1	1.02	0.9	2.75	-2.75
5	sfHSD	0	0.33, 0.67, 1	1.11	0.9	2.4	-2.4
6	sfHSD	0	0.5, 0.75, 1	1.11	0.9	2.24	-2.24
8	sfHSD	4	0.5, 0.75, 1	1.35	0.9	2.01	-2.01
7	sfHSD	4	0.33, 0.67, 1	1.37	0.9	2.08	-2.08

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()
job_surv$table(n = 6)

Config ID	Upper bound	Timing	HR	Final events	Final total N	Power	Upper Z (IA1)	Lower Z (IA1)
1	sfLDOF	0.33, 0.67, 1	0.6	170	296	0.9	3.73	-0.719
2	sfLDOF	0.33, 0.67, 1	0.675	288	482	0.9	3.73	-0.719
3	sfLDOF	0.33, 0.67, 1	0.75	538	874	0.9	3.73	-0.719
4	sfLDOF	0.5, 0.75, 1	0.6	174	302	0.9	2.96	0.332
5	sfLDOF	0.5, 0.75, 1	0.675	294	492	0.9	2.96	0.332
6	sfLDOF	0.5, 0.75, 1	0.75	549	892	0.9	2.96	0.332

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()
job_cal$table(n = 6)

Config ID	Upper bound	Calendar time	Spending	HR	Final events	Final total N	Power	Upper Z (IA1)	Lower Z (IA1)
1	sfLDOF	12, 24, 36	information	0.6	173	212	0.9	4.48	-1.565
2	sfLDOF	12, 24, 36	information	0.675	293	354	0.9	4.44	-1.516
3	sfLDOF	12, 24, 36	information	0.75	549	650	0.9	4.39	-1.467
4	sfLDOF	12, 24, 36	calendar	0.6	165	204	0.9	3.71	-1.023
5	sfLDOF	12, 24, 36	calendar	0.675	279	336	0.9	3.71	-1.01
6	sfLDOF	12, 24, 36	calendar	0.75	522	618	0.9	3.71	-0.996

Multi-scenario exploration

best_surv <- job_surv$best("final_events", direction = "min")
job_surv$table(best_surv, n = 10)

Config ID	Upper bound	Upper parameter	Timing	HR	Final events	Final total N	Power	Upper Z (IA1)	Lower Z (IA1)
1	sfLDOF	0	0.33, 0.67, 1	0.6	170	296	0.9	3.73	-0.719
7	sfHSD	-4	0.33, 0.67, 1	0.6	171	296	0.9	3.02	-0.716
4	sfLDOF	0	0.5, 0.75, 1	0.6	174	302	0.9	2.96	0.332
10	sfHSD	-4	0.5, 0.75, 1	0.6	174	302	0.9	2.75	0.332
13	sfHSD	0	0.33, 0.67, 1	0.6	185	320	0.9	2.4	-0.638
16	sfHSD	0	0.5, 0.75, 1	0.6	188	326	0.9	2.24	0.423
22	sfHSD	4	0.5, 0.75, 1	0.6	224	388	0.9	2.01	0.651
19	sfHSD	4	0.33, 0.67, 1	0.6	226	392	0.9	2.08	-0.428
2	sfLDOF	0	0.33, 0.67, 1	0.675	288	482	0.9	3.73	-0.719
8	sfHSD	-4	0.33, 0.67, 1	0.675	289	484	0.9	3.02	-0.716

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] "/tmp/RtmpSDoSXC/file1c1a78427626.html"

Basic designs with gsDesignTune()