TrialSimulator (version 1.7.0)

GroupSequentialTest: Class of GroupSequentialTest

Description

Perform group sequential test for a single endpoint based on sequential one-sided p-values at each stages. Selected alpha spending functions, including user-defined functions, are supported. Boundaries are calculated with `rpact`. At the final analysis, adjustment can be applied for over-running or under-running trial where observed final information is greater or lower than the planned maximum information. See Wassmer & Brannath, 2016, p78f. The test is based on p-values not z statistics because it is easier to not handling direction of alternative hypothesis in current implementation. In addition, only one-sided test is supported which should be sufficient for common use in clinical design.

Methods

Public methods

• GroupSequentialTest$new()

• GroupSequentialTest$get_name()

• GroupSequentialTest$get_alpha()

• GroupSequentialTest$set_alpha_spending()

• GroupSequentialTest$get_alpha_spending()

• GroupSequentialTest$get_max_info()

• GroupSequentialTest$set_max_info()

• GroupSequentialTest$get_stage()

• GroupSequentialTest$reset()

• GroupSequentialTest$set_trajectory()

• GroupSequentialTest$get_trajectory()

• GroupSequentialTest$get_stage_level()

• GroupSequentialTest$test_one()

• GroupSequentialTest$test()

• GroupSequentialTest$print()

• GroupSequentialTest$clone()

---

Method new()

initialize a group sequential test. Now only support one-sided test based on p-values.

Usage

GroupSequentialTest$new(
  alpha = 0.025,
  alpha_spending = c("asP", "asOF", "asUser"),
  planned_max_info,
  name = "H0",
  silent = TRUE
)

Arguments

alpha

familywise error rate

alpha_spending

alpha spending function. Use "asUser" if custom alpha spending schedule is used.

planned_max_info

integer. Planned maximum number of patients for non-tte endpoints or number of events for tte endpoints

name

character. Name of the hypothesis, e.g. endpoint, subgroup, etc. Optional.

silent

TRUE if muting all messages.

---

Method get_name()

get name of hypothesis

Usage

GroupSequentialTest$get_name()

---

Method get_alpha()

get overall alpha

Usage

GroupSequentialTest$get_alpha()

---

Method set_alpha_spending()

set alpha spending function. This is useful when set 'asUser' at the final stage to adjust for an under- or over-running trial.

Usage

GroupSequentialTest$set_alpha_spending(asf)

Arguments

asf

character of alpha spending function.

---

Method get_alpha_spending()

return character of alpha spending function

Usage

GroupSequentialTest$get_alpha_spending()

---

Method get_max_info()

return planned maximum information

Usage

GroupSequentialTest$get_max_info()

---

Method set_max_info()

set planned maximum information. This is used at the final stage to adjust for an under- or over-running trial.

Usage

GroupSequentialTest$set_max_info(obs_max_info)

Arguments

obs_max_info

integer. Maximum information, which could be observed number of patients or events at the final stage.

---

Method get_stage()

get current stage.

Usage

GroupSequentialTest$get_stage()

---

Method reset()

an object of class GroupSequentialTest is designed to be used sequentially by calling GroupSequentialTest$test. When all planned tests are performed, no further analysis could be done. In that case keep calling GroupSequentialTest$test will trigger an error. To reuse the object for a new set of staged p-values, call this function to reset the status to stage 1. See examples. This implementation can prevent the error that more than the planned number of stages are tested.

Usage

GroupSequentialTest$reset()

---

Method set_trajectory()

save testing result at current stage

Usage

GroupSequentialTest$set_trajectory(result, is_final = FALSE)

Arguments

result

a data frame storing testing result at a stage.

is_final

logical. TRUE if final test for the hypothesis, FALSE otherwise.

---

Method get_trajectory()

return testing trajectory until current stage. This function can be called at any stage. See examples.

Usage

GroupSequentialTest$get_trajectory()

---

Method get_stage_level()

compute boundaries given current (potentially updated) settings. It returns different values if settings are changed over time.

Usage

GroupSequentialTest$get_stage_level()

---

Method test_one()

test a hypothesis with the given p-value at current stage

Usage

GroupSequentialTest$test_one(
  p_value,
  is_final,
  observed_info,
  alpha_spent = NA_real_
)

Arguments

p_value

numeric. A p-value.

is_final

logical. TRUE if this test is carried out for the final analysis.

observed_info

integer. Observed information at current stage. It can be the number of samples (non-tte) or number of events (tte) at test. If the current stage is final, observed_info will be used to update planned_max_info, the alpha spending function (typeOfDesign in rpact) will be updated to 'asUser', and the argument userAlphaSpending will be used when calling rpact::getDesignGroupSequential.

alpha_spent

numeric if alpha_spending = "asUser". It must be between 0 and alpha, the overall alpha of the test. NA_real_ for other alpha spending functions "asOF" and "asP".

---

Method test()

Carry out test based on group sequential design. If p_values is NULL, dummy values will be use and boundaries are calculated for users to review.

Usage

GroupSequentialTest$test(
  observed_info,
  is_final,
  p_values = NULL,
  alpha_spent = NULL
)

Arguments

observed_info

a vector of integers, observed information at stages.

is_final

logical vector. TRUE if the test is for the final analysis.

p_values

a vector of p-values. If specified, its length should equal to the length of observed_info.

alpha_spent

accumulative alpha spent at observed information. It is a numeric vector of values between 0 and 1, and of length that equals length(observed_info) if alpha-spending function is "asUser". Otherwise NULL.

---

Method print()

generic function for print

Usage

GroupSequentialTest$print()

---

Method clone()

The objects of this class are cloneable with this method.

Usage

GroupSequentialTest$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

## Note: examples showed here replicate the results from
## https://www.rpact.org/vignettes/planning/rpact_boundary_update_example/

## Example 1. Generate boundaries for a pre-fix group sequential design
gst <- GroupSequentialTest$new(
  alpha = .025, alpha_spending = 'asOF',
  planned_max_info = 387)

## without giving p-values, boundaries are returned without actual testing
gst$test(observed_info = c(205, 285, 393), is_final = c(FALSE, FALSE, TRUE))
gst

## Example 2. Calculate boundaries with observed information at stages
## No p-values are provided

## get an error without resetting an used object
try( gst$test(observed_info = 500, is_final = FALSE) )

## reset the object for re-use
gst$reset()
gst$test(observed_info = c(205, 285, 393), is_final = c(FALSE, FALSE, TRUE))
gst

## Example 3. Test stagewise p-values sequentially
gst$reset()

gst$test(observed_info = 205, is_final = FALSE, p_values = .09)
gst$test(285, FALSE, .006)

## print testing trajectory by now
gst

gst$test(393, TRUE, .002)

## print all testing trajectory
gst

## you can also test all stages at once
## the result is the same as calling test() for each of the stages
gst$reset()
gst$test(c(205, 285, 393), c(FALSE, FALSE, TRUE), c(.09, .006, .002))
gst

## Example 4. use user-define alpha spending
gst <- GroupSequentialTest$new(
  alpha = .025, alpha_spending = 'asUser',
  planned_max_info = 387)

gst$test(
  observed_info = c(205, 285, 393),
  is_final = c(FALSE, FALSE, TRUE),
  alpha_spent = c(.005, .0125, .025))
gst