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Introduction

We compare derivation of different spending bounds using the gsDesign2 and gsDesign packages. In gsDesign, there are 6 types of bounds. We demonstrate here how to replicate these using gsDesign2. In gsDesign2, the gs_spending_bound() function can be used to derive spending boundaries for all group sequential design derivations and power calculations. We demonstrate with the gs_design_ahr() function here, using designs under proportional hazards assumptions to compare with gsDesign::gsSurv(). Since the sample size methods differ between the gsDesign2::gs_design_ahr() and gsDesign::gsSurv() functions, we use continuous sample sizes so that spending bounds (Z-values, nominal p-values, spending) should be identical except where noted. Indeed, we are able to reproduce bounds to a high degree of accuracy. Due to the different sample size methods, sample size and other boundary approximations vary slightly.

We also present a seventh example to implement a futility bound based on observed hazard ratio as well as a Haybittle-Peto-like efficacy bound. In particular, the futility bound would be difficult to implement using the gsDesign package while it is straightforward using gsDesign2.

For the last two examples, we implement integer sample size and event counts using the to_integer() function for the gsDesign2 package and the toInteger() function for the gsDesign package. This would generally would be used for all cases other than when we are comparing package computations as in Examples 1–5.

For all of our examples, we will use the following design assumptions:

trial_duration <- 36 # Planned trial duration
info_frac <- c(.35, .7, 1) # Information fraction at analyses
# 16 month planned enrollment with constant rate
enroll_rate <- define_enroll_rate(duration = 16, rate = 1)
# Minimum follow-up for gsSurv() (computed)
minfup <- trial_duration - sum(enroll_rate$duration)
# Failure rates
fail_rate <- define_fail_rate(
  duration = Inf, # Single time period, exponential failure
  fail_rate = log(2) / 12, # Exponential time-to-event with 12 month median
  hr = .7, # Proportional hazards
  dropout_rate = -log(.99) / 12 # 1% dropout rate per year
)
alpha <- 0.025 # Type I error (one-sided)
beta <- 0.15 # 85% power = 15% Type II error
ratio <- 1 # Randomization ratio (experimental / control)

The choice of Type II error of 0.15 corresponding to 85% power is intentional. This allows for more impactful futility bounds at interim analyses. Many teams may decide on the more typical 90% power (beta = .1), but this can make futility bounds less likely to impact early decisions.

Examples

Analogous to the gsDesign package, we look at 6 variations on combinations of efficacy and futility bounds.

Example 1: Efficacy bound only

One-sided design has only an efficacy bound. An easy way to do this is to use a fixed bound (lower = gs_b) with negative infinite bounds (lpar = rep(-Inf, 3)); in the summary table produced, infinite bounds do not appear. The upper bound implements a spending bound (upper = gs_spending_bound) and the list of objects provided in upar describe the spending function and any associated parameters. The only parts of the upar list used here are sf = gsDesign::sfLDOF to select a Lan-DeMets spending function that approximates an O’Brien-Fleming bound. The total_spend = alpha sets the total spending to the targeted Type I error for the study. The upper bound provides the Type I error control for the design as it is not specified elsewhere.

upar <- list(sf = gsDesign::sfLDOF, total_spend = alpha, param = NULL)

one_sided <- gsDesign2::gs_design_ahr(
  enroll_rate = enroll_rate, fail_rate = fail_rate,
  ratio = ratio, beta = beta,
  # Information fraction at analyses and trial duration
  info_frac = info_frac, analysis_time = trial_duration,
  # Precision parameters for computations
  r = 32, tol = 1e-08,
  # Use NULL information for Type I error, H1 information for Type II error (power)
  info_scale = "h0_h1_info", # Default
  # Upper spending bound and corresponding parameter(s)
  upper = gs_spending_bound, upar = upar,
  # No lower bound
  lower = gs_b, lpar = rep(-Inf, 3)
)

one_sided |>
  summary() |>
  gsDesign2::as_gt(title = "Efficacy bound only", subtitle = "alpha-spending")
Efficacy bound only
alpha-spending
Bound Z Nominal p1 ~HR at bound2 Cumulative boundary crossing probability
Alternate hypothesis Null hypothesis
Analysis: 1 Time: 14.5 N: 356 Event: 100.2 AHR: 0.7 Information fraction: 0.35
Efficacy 3.61 0.0002 0.4858 0.0352 0.0002
Analysis: 2 Time: 23.3 N: 393.9 Event: 200.4 AHR: 0.7 Information fraction: 0.7
Efficacy 2.44 0.0073 0.7083 0.5295 0.0074
Analysis: 3 Time: 36 N: 393.9 Event: 286.2 AHR: 0.7 Information fraction: 1
Efficacy 2.00 0.0227 0.7894 0.8500 0.0250
1 One-sided p-value for experimental vs control treatment. Value < 0.5 favors experimental, > 0.5 favors control.
2 Approximate hazard ratio to cross bound.

Now we check this with gsDesign::gsSurv(). As noted above, sample size and event counts vary slightly from the design derived using gs_design_ahr(). This also results in slightly different crossing probabilities under the alternate hypothesis at interim analyses as well as slightly different approximate hazard ratios required to cross bounds.

oneSided <- gsSurv(
  alpha = alpha, beta = beta, timing = info_frac, T = trial_duration, minfup = minfup,
  lambdaC = fail_rate$fail_rate, eta = fail_rate$dropout_rate, hr = fail_rate$hr,
  r = 32, tol = 1e-08, # Precision parameters for computations
  test.type = 1, # One-sided bound; efficacy only
  # Upper bound parameters
  sfu = upar$sf, sfupar = upar$param,
)
oneSided |> gsBoundSummary()
#>     Analysis              Value Efficacy
#>    IA 1: 35%                  Z   3.6128
#>       N: 356        p (1-sided)   0.0002
#>  Events: 100       ~HR at bound   0.4852
#>    Month: 14   P(Cross) if HR=1   0.0002
#>              P(Cross) if HR=0.7   0.0338
#>    IA 2: 70%                  Z   2.4406
#>       N: 394        p (1-sided)   0.0073
#>  Events: 200       ~HR at bound   0.7079
#>    Month: 23   P(Cross) if HR=1   0.0074
#>              P(Cross) if HR=0.7   0.5341
#>        Final                  Z   2.0002
#>       N: 394        p (1-sided)   0.0227
#>  Events: 286       ~HR at bound   0.7891
#>    Month: 36   P(Cross) if HR=1   0.0250
#>              P(Cross) if HR=0.7   0.8500

Comparing Z-value bounds directly we see they are the same through approximately 6 digits with precision parameters chosen (r=32, tol=1e-08):

one_sided$bound$z - oneSided$upper$bound
#> [1] -1.349247e-07  9.218765e-07  3.515345e-07

Example 2: Symmetric 2-sided design

We now derive a symmetric 2-sided design. This requires use of the argument h1_spending = FALSE to use \alpha-spending for both the upper and lower bounds. While the lower bound is labeled as a futility bound in the table, it would be better termed an efficacy bound for control better than experimental treatment.

upar <- list(sf = gsDesign::sfLDOF, total_spend = alpha, param = NULL)
lpar <- list(sf = gsDesign::sfLDOF, total_spend = alpha, param = NULL)

symmetric <- gs_design_ahr(
  enroll_rate = enroll_rate, fail_rate = fail_rate,
  ratio = ratio, beta = beta,
  # Information fraction at analyses and trial duration
  info_frac = info_frac, analysis_time = trial_duration,
  # Precision parameters for computations
  r = 32, tol = 1e-08,
  # Use NULL information for Type I error, H1 information for power
  info_scale = "h0_h1_info", # Default
  # Function and parameter(s) for upper spending bound
  upper = gs_spending_bound, upar = upar,
  lower = gs_spending_bound, lpar = lpar,
  # Symmetric designs use binding bounds
  binding = TRUE,
  h1_spending = FALSE # Use null hypothesis spending for lower bound
)

symmetric |>
  summary() |>
  gsDesign2::as_gt(
    title = "2-sided Symmetric Design",
    subtitle = "Single spending function"
  )
2-sided Symmetric Design
Single spending function
Bound Z Nominal p1 ~HR at bound2 Cumulative boundary crossing probability
Alternate hypothesis Null hypothesis
Analysis: 1 Time: 14.5 N: 356 Event: 100.2 AHR: 0.7 Information fraction: 0.35
Futility -3.61 0.9998 2.0584 0.0000 0.0002
Efficacy 3.61 0.0002 0.4858 0.0352 0.0002
Analysis: 2 Time: 23.3 N: 393.9 Event: 200.4 AHR: 0.7 Information fraction: 0.7
Futility -2.44 0.9927 1.4118 0.0000 0.0074
Efficacy 2.44 0.0073 0.7083 0.5295 0.0074
Analysis: 3 Time: 36 N: 393.9 Event: 286.2 AHR: 0.7 Information fraction: 1
Futility -2.00 0.9773 1.2668 0.0000 0.0250
Efficacy 2.00 0.0227 0.7894 0.8500 0.0250
1 One-sided p-value for experimental vs control treatment. Value < 0.5 favors experimental, > 0.5 favors control.
2 Approximate hazard ratio to cross bound.

We compare with gsDesign::gsSurv().

Symmetric <-
  gsSurv(
    test.type = 2, # Two-sided symmetric bound
    alpha = alpha, beta = beta, timing = info_frac, T = trial_duration, minfup = minfup, r = 32, tol = 1e-08,
    lambdaC = fail_rate$fail_rate, eta = fail_rate$dropout_rate, hr = fail_rate$hr,
    sfu = upar$sf, sfupar = upar$param
  )
Symmetric |> gsBoundSummary()
#>     Analysis              Value Efficacy Futility
#>    IA 1: 35%                  Z   3.6128  -3.6128
#>       N: 356        p (1-sided)   0.0002   0.0002
#>  Events: 100       ~HR at bound   0.4852   2.0609
#>    Month: 14   P(Cross) if HR=1   0.0002   0.0002
#>              P(Cross) if HR=0.7   0.0338   0.0000
#>    IA 2: 70%                  Z   2.4406  -2.4406
#>       N: 394        p (1-sided)   0.0073   0.0073
#>  Events: 200       ~HR at bound   0.7079   1.4126
#>    Month: 23   P(Cross) if HR=1   0.0074   0.0074
#>              P(Cross) if HR=0.7   0.5341   0.0000
#>        Final                  Z   2.0002  -2.0002
#>       N: 394        p (1-sided)   0.0227   0.0227
#>  Events: 286       ~HR at bound   0.7891   1.2673
#>    Month: 36   P(Cross) if HR=1   0.0250   0.0250
#>              P(Cross) if HR=0.7   0.8500   0.0000

Comparing Z-value bounds directly, we again see approximately 6 digits of accuracy.

dplyr::filter(symmetric$bound, bound == "upper")$z - Symmetric$upper$bound
#> [1] -1.349247e-07  9.218765e-07  4.092976e-07
dplyr::filter(symmetric$bound, bound == "lower")$z - Symmetric$lower$bound
#> [1]  1.349247e-07 -9.218765e-07 -4.092976e-07

Example 3: Asymmetric 2-sided design with \beta-spending and binding futility

Designs with binding futility bounds are generally not considered acceptable for Phase 3 trials as Type I error is not controlled if a futility bound is crossed and the trial continues, a not infrequent occurrence. A binding futility bound means that Type I error computations assume that a trial stops when a futility bound is crossed. If the trial continues after a futility bound has been crossed, Type I error is no longer controlled with the computed efficacy bound. For a Phase 2b study, this may be acceptable and results in a slightly smaller sample size and less stringent efficacy bounds after the first analysis than a comparable design with a non-binding futility bound presented in Example 4.

upar <- list(sf = gsDesign::sfLDOF, total_spend = alpha, param = NULL)
lpar <- list(sf = gsDesign::sfHSD, total_spend = beta, param = -.5)

asymmetric_binding <- gs_design_ahr(
  enroll_rate = enroll_rate, fail_rate = fail_rate,
  ratio = ratio, beta = beta,
  # Information fraction at analyses and trial duration
  info_frac = info_frac, analysis_time = trial_duration,
  # Precision parameters for computations
  r = 32, tol = 1e-08,
  # Use NULL information for Type I error, H1 information for Type II error and power
  info_scale = "h0_h1_info",
  # Function and parameter(s) for upper spending bound
  upper = gs_spending_bound, upar = upar,
  lower = gs_spending_bound, lpar = lpar,
  # Asymmetric beta-spending design using binding bounds
  binding = TRUE,
  h1_spending = TRUE # Use beta-spending for futility
)

asymmetric_binding |>
  summary() |>
  gsDesign2::as_gt(
    title = "2-sided asymmetric design with binding futility",
    subtitle = "Both alpha- and beta-spending used"
  )
2-sided asymmetric design with binding futility
Both alpha- and beta-spending used
Bound Z Nominal p1 ~HR at bound2 Cumulative boundary crossing probability
Alternate hypothesis Null hypothesis
Analysis: 1 Time: 14.5 N: 380 Event: 106.9 AHR: 0.7 Information fraction: 0.35
Futility 0.12 0.4540 0.9779 0.0435 0.5460
Efficacy 3.61 0.0002 0.4972 0.0400 0.0002
Analysis: 2 Time: 23.3 N: 420.5 Event: 213.9 AHR: 0.7 Information fraction: 0.7
Futility 1.15 0.1243 0.8540 0.0960 0.8861
Efficacy 2.44 0.0074 0.7164 0.5621 0.0074
Analysis: 3 Time: 36 N: 420.5 Event: 305.5 AHR: 0.7 Information fraction: 1
Futility 1.91 0.0282 0.8039 0.1499 0.9740
Efficacy 1.93 0.0268 0.8019 0.8500 0.0250
1 One-sided p-value for experimental vs control treatment. Value < 0.5 favors experimental, > 0.5 favors control.
2 Approximate hazard ratio to cross bound.

We compare with gsDesign::gsSurv().

asymmetricBinding <- gsSurv(
  test.type = 3, # Two-sided asymmetric bound, binding futility
  alpha = alpha, beta = beta, timing = info_frac, T = trial_duration, minfup = minfup, r = 32, tol = 1e-07,
  lambdaC = fail_rate$fail_rate, eta = fail_rate$dropout_rate, hr = fail_rate$hr,
  sfu = upar$sf, sfupar = upar$param, sfl = lpar$sf, sflpar = lpar$param
)
asymmetricBinding |> gsBoundSummary()
#>     Analysis              Value Efficacy Futility
#>    IA 1: 35%                  Z   3.6128   0.1436
#>       N: 380        p (1-sided)   0.0002   0.4429
#>  Events: 107       ~HR at bound   0.4971   0.9726
#>    Month: 14   P(Cross) if HR=1   0.0002   0.5571
#>              P(Cross) if HR=0.7   0.0387   0.0442
#>    IA 2: 70%                  Z   2.4382   1.1807
#>       N: 422        p (1-sided)   0.0074   0.1189
#>  Events: 214       ~HR at bound   0.7164   0.8509
#>    Month: 23   P(Cross) if HR=1   0.0074   0.8913
#>              P(Cross) if HR=0.7   0.5679   0.0969
#>        Final                  Z   1.9232   1.9232
#>       N: 422        p (1-sided)   0.0272   0.0272
#>  Events: 306       ~HR at bound   0.8024   0.8024
#>    Month: 36   P(Cross) if HR=1   0.0250   0.9750
#>              P(Cross) if HR=0.7   0.8500   0.1500

Comparing Z-value bounds directly, we again see approximately 6 digits of accuracy in spite of needing to relaxing accuracy to tol = 1e-07 in the call to gsSurv() in order to get convergence.

dplyr::filter(asymmetric_binding$bound, bound == "upper")$z - asymmetricBinding$upper$bound
#> [1] -1.349247e-07  2.505886e-04  6.494369e-03
dplyr::filter(asymmetric_binding$bound, bound == "lower")$z - asymmetricBinding$lower$bound
#> [1] -0.02803415 -0.02670908 -0.01598640

Example 4: Asymmetric 2-sided design with \beta-spending and non-binding futility bound

In the gsDesign package, asymmetric designs with non-binding \beta-spending used for futility are the default design. The objectives of this type of design include:

  • Meaningful futility bounds to stop a trial early if no treatment benefit is emerging for the experimental treatment vs. control.
  • Type I error is controlled even if the trial continues after a futility bound is crossed.
upar <- list(sf = gsDesign::sfLDOF, total_spend = alpha, param = NULL)
lpar <- list(sf = gsDesign::sfHSD, total_spend = beta, param = -.5)

asymmetric_nonbinding <- gs_design_ahr(
  enroll_rate = enroll_rate, fail_rate = fail_rate,
  ratio = ratio, beta = beta,
  # Information fraction at analyses and trial duration
  info_frac = info_frac, analysis_time = trial_duration,
  # Precision parameters for computations
  r = 32, tol = 1e-08,
  # Use NULL information for Type I error, H1 info for Type II error and power
  info_scale = "h0_h1_info", # Default
  # Function and parameter(s) for upper spending bound
  upper = gs_spending_bound, upar = upar,
  lower = gs_spending_bound, lpar = lpar,
  # Asymmetric beta-spending design use binding bounds
  binding = FALSE,
  h1_spending = TRUE # Use beta-spending for futility
)

asymmetric_nonbinding |>
  summary() |>
  gsDesign2::as_gt(
    title = "2-sided asymmetric design with non-binding futility",
    subtitle = "Both alpha- and beta-spending used"
  )
2-sided asymmetric design with non-binding futility
Both alpha- and beta-spending used
Bound Z Nominal p1 ~HR at bound2 Cumulative boundary crossing probability
Alternate hypothesis Null hypothesis
Analysis: 1 Time: 14.5 N: 395.9 Event: 111.4 AHR: 0.7 Information fraction: 0.35
Futility 0.15 0.4391 0.9714 0.0435 0.5609
Efficacy 3.61 0.0002 0.5043 0.0433 0.0002
Analysis: 2 Time: 23.3 N: 438.1 Event: 222.8 AHR: 0.7 Information fraction: 0.7
Futility 1.21 0.1136 0.8506 0.0960 0.8960
Efficacy 2.44 0.0073 0.7211 0.5822 0.0073
Analysis: 3 Time: 36 N: 438.1 Event: 318.3 AHR: 0.7 Information fraction: 1
Futility 1.98 0.0241 0.8013 0.1499 0.9773
Efficacy 2.00 0.0227 0.7991 0.8500 3 0.0218
1 One-sided p-value for experimental vs control treatment. Value < 0.5 favors experimental, > 0.5 favors control.
2 Approximate hazard ratio to cross bound.
3 Cumulative alpha for final analysis (0.0218) is less than the full alpha (0.025) when the futility bound is non-binding. The smaller value subtracts the probability of crossing a futility bound before crossing an efficacy bound at a later analysis (0.025 - 0.0032 = 0.0218) under the null hypothesis.

We compare with gsDesign::gsSurv().

asymmetricNonBinding <- gsSurv(
  test.type = 4, # Two-sided asymmetric bound, non-binding futility
  alpha = alpha, beta = beta, timing = info_frac, T = trial_duration, minfup = minfup, r = 32, tol = 1e-08,
  lambdaC = fail_rate$fail_rate, eta = fail_rate$dropout_rate, hr = fail_rate$hr,
  sfu = upar$sf, sfupar = upar$param, sfl = lpar$sf, sflpar = lpar$param
)
asymmetricNonBinding |> gsBoundSummary()
#>     Analysis              Value Efficacy Futility
#>    IA 1: 35%                  Z   3.6128   0.1860
#>       N: 398        p (1-sided)   0.0002   0.4262
#>  Events: 112       ~HR at bound   0.5050   0.9654
#>    Month: 14   P(Cross) if HR=1   0.0002   0.5738
#>              P(Cross) if HR=0.7   0.0424   0.0442
#>    IA 2: 70%                  Z   2.4406   1.2406
#>       N: 440        p (1-sided)   0.0073   0.1074
#>  Events: 224       ~HR at bound   0.7215   0.8471
#>    Month: 23   P(Cross) if HR=1   0.0073   0.9020
#>              P(Cross) if HR=0.7   0.5901   0.0969
#>        Final                  Z   2.0002   2.0002
#>       N: 440        p (1-sided)   0.0227   0.0227
#>  Events: 320       ~HR at bound   0.7995   0.7995
#>    Month: 36   P(Cross) if HR=1   0.0215   0.9785
#>              P(Cross) if HR=0.7   0.8500   0.1500

Comparing Z-value bounds directly, we again see approximately 6 digits of accuracy.

dplyr::filter(asymmetric_nonbinding$bound, bound == "upper")$z - asymmetricNonBinding$upper$bound
#> [1] -1.349247e-07  9.218765e-07  3.515345e-07
dplyr::filter(asymmetric_nonbinding$bound, bound == "lower")$z - asymmetricNonBinding$lower$bound
#> [1] -0.03267431 -0.03311078 -0.02426999

Example 5: Asymmetric 2-sided design with null hypothesis spending and binding futility bound

Now we use null hypothesis probabilities to set futility bounds. The parameter alpha_star is used to set the total spending for the futility bound under the null hypothesis. For our example, this is set to 0.5 which is a 50% probability of crossing the futility bound at the interim and final analyses combined. The futility bound at the final analysis really has no role, so we use the test_lower argument to eliminate this evaluation at the final analysis. This is arbitrary and largely selected so that the interim futility bounds can be meaningful tests. In this case, more than a minor trend in favor of control at the first or second interim will cross a futility bound. This is less stringent than the \beta-spending bounds previously described, but still address a potential ethical issue of continuing the trial when more than a minor trend in favor of control is present.

alpha_star <- .5
upar <- list(sf = gsDesign::sfLDOF, total_spend = alpha, param = NULL)
lpar <- list(sf = gsDesign::sfHSD, total_spend = alpha_star, param = 1)

asymmetric_safety_binding <- gs_design_ahr(
  enroll_rate = enroll_rate, fail_rate = fail_rate,
  ratio = ratio, beta = beta,
  # Information fraction at analyses and trial duration
  info_frac = info_frac, analysis_time = trial_duration,
  # Precision parameters for computations
  r = 32, tol = 1e-08,
  # Use NULL information for Type I error, H1 information for Type II error
  info_scale = "h0_info",
  # Function and parameter(s) for upper spending bound
  upper = gs_spending_bound, upar = upar,
  lower = gs_spending_bound, lpar = lpar,
  test_lower = c(TRUE, TRUE, FALSE),
  # Asymmetric design use binding bounds
  binding = TRUE,
  h1_spending = FALSE # Use null-spending for futility
)

asymmetric_safety_binding |>
  summary() |>
  gsDesign2::as_gt(
    title = "2-sided asymmetric safety design with binding futility",
    subtitle = "Alpha-spending used for both bounds, asymmetrically"
  )
2-sided asymmetric safety design with binding futility
Alpha-spending used for both bounds, asymmetrically
Bound Z Nominal p1 ~HR at bound2 Cumulative boundary crossing probability
Alternate hypothesis Null hypothesis
Analysis: 1 Time: 14.5 N: 359.6 Event: 101.2 AHR: 0.7 Information fraction: 0.35
Futility -0.73 0.7664 1.1561 0.0060 0.2336
Efficacy 3.61 0.0002 0.4863 0.0340 0.0002
Analysis: 2 Time: 23.3 N: 397.9 Event: 202.4 AHR: 0.7 Information fraction: 0.7
Futility -0.42 0.6629 1.0611 0.0070 0.3982
Efficacy 2.44 0.0073 0.7087 0.5353 0.0074
Analysis: 3 Time: 36 N: 397.9 Event: 289.1 AHR: 0.7 Information fraction: 1
Efficacy 2.00 0.0229 0.7899 0.8500 0.0250
1 One-sided p-value for experimental vs control treatment. Value < 0.5 favors experimental, > 0.5 favors control.
2 Approximate hazard ratio to cross bound.
asymmetricSafetyBinding <- gsSurv(
  test.type = 5, # Two-sided asymmetric bound, binding futility, H0 futility spending
  astar = alpha_star, # Total Type I error spend for futility
  alpha = alpha, beta = beta, timing = info_frac, T = trial_duration, minfup = minfup,
  lambdaC = fail_rate$fail_rate, eta = fail_rate$dropout_rate, hr = fail_rate$hr,
  sfu = upar$sf, sfupar = upar$param, sfl = lpar$sf, sflpar = lpar$param
)
asymmetricSafetyBinding |> gsBoundSummary()
#>     Analysis              Value Efficacy Futility
#>    IA 1: 35%                  Z   3.6128  -0.7271
#>       N: 356        p (1-sided)   0.0002   0.7664
#>  Events: 101       ~HR at bound   0.4856   1.1565
#>    Month: 14   P(Cross) if HR=1   0.0002   0.2336
#>              P(Cross) if HR=0.7   0.0340   0.0060
#>    IA 2: 70%                  Z   2.4405  -0.4203
#>       N: 394        p (1-sided)   0.0073   0.6629
#>  Events: 201       ~HR at bound   0.7082   1.0612
#>    Month: 23   P(Cross) if HR=1   0.0074   0.3982
#>              P(Cross) if HR=0.7   0.5353   0.0070
#>        Final                  Z   1.9979  -0.2531
#>       N: 394        p (1-sided)   0.0229   0.5999
#>  Events: 286       ~HR at bound   0.7895   1.0304
#>    Month: 36   P(Cross) if HR=1   0.0250   0.5000
#>              P(Cross) if HR=0.7   0.8500   0.0072

Comparing Z-value bounds directly, we again see approximately 6 digits of accuracy. For gsSurv() this did not require the alternate arguments for r and tol.

dplyr::filter(asymmetric_safety_binding$bound, bound == "upper")$z - asymmetricSafetyBinding$upper$bound
#> [1] -1.349247e-07  9.211210e-07  4.185954e-07
dplyr::filter(asymmetric_safety_binding$bound, bound == "lower")$z - asymmetricSafetyBinding$lower$bound[1:2]
#> [1]  4.348992e-08 -3.276118e-08

Example 6: Asymmetric 2-sided design with null hypothesis spending and non-binding futility bound

Again, we would recommend a non-binding bound presented here over the binding bound in example 5. We again eliminate the final futility bound using the test_lower argument. Addition, we show how to eliminate the efficacy bound at interim 1 allowing a team to decide that it is too early to stop a trial for efficacy without longer-term data.

upar <- list(sf = gsDesign::sfLDOF, total_spend = alpha, param = NULL)
lpar <- list(sf = gsDesign::sfHSD, total_spend = alpha_star, param = 1)

asymmetric_safety_nonbinding <- gs_design_ahr(
  enroll_rate = enroll_rate, fail_rate = fail_rate,
  ratio = ratio, beta = beta,
  # Information fraction at analyses and trial duration
  info_frac = info_frac, analysis_time = trial_duration,
  # Precision parameters for computations
  r = 32, tol = 1e-08,
  # Use NULL information for Type I error, H1 information for Type II error
  info_scale = "h0_info",
  # Function and parameter(s) for upper spending bound
  upper = gs_spending_bound, upar = upar,
  test_upper = c(FALSE, TRUE, TRUE),
  lower = gs_spending_bound, lpar = lpar,
  test_lower = c(TRUE, TRUE, FALSE),
  # Asymmetric design use non-binding bounds
  binding = FALSE,
  h1_spending = FALSE # Use null-spending for futility
) |> to_integer()

asymmetric_safety_nonbinding |>
  summary() |>
  gsDesign2::as_gt(
    title = "2-sided asymmetric safety design with non-binding futility",
    subtitle = "Alpha-spending used for both bounds, asymmetrically"
  ) |>
  gt::tab_footnote(footnote = "Integer-based sample size and event counts")
2-sided asymmetric safety design with non-binding futility
Alpha-spending used for both bounds, asymmetrically
Bound Z Nominal p1 ~HR at bound2 Cumulative boundary crossing probability
Alternate hypothesis Null hypothesis
Analysis: 1 Time: 14.4 N: 360 Event: 101 AHR: 0.7 Information fraction: 0.35
Futility 1.05 0.1460 0.8108 0.2301 0.8540
Analysis: 2 Time: 23.1 N: 400 Event: 202 AHR: 0.7 Information fraction: 0.7
Futility 2.11 0.0175 0.7433 0.3950 0.9856
Efficacy 2.46 0.0070 0.7079 0.5002 0.0063
Analysis: 3 Time: 35.9 N: 400 Event: 290 AHR: 0.7 Information fraction: 1
Efficacy 2.00 0.0229 0.7909 0.5980 3 0.0097
Integer-based sample size and event counts
1 One-sided p-value for experimental vs control treatment. Value < 0.5 favors experimental, > 0.5 favors control.
2 Approximate hazard ratio to cross bound.
3 Cumulative alpha for final analysis (0.0097) is less than the full alpha (0.025) when the futility bound is non-binding. The smaller value subtracts the probability of crossing a futility bound before crossing an efficacy bound at a later analysis (0.025 - 0.0153 = 0.0097) under the null hypothesis.

The corresponding gsDesign::gsSurv() design is not strictly comparable since the option to eliminate some futility and efficacy analyses is not enabled.

asymmetricSafetyNonBinding <- gsSurv(
  test.type = 6, # Two-sided asymmetric bound, binding futility, H0 futility spending
  astar = alpha_star, # Total Type I error spend for futility
  alpha = alpha, beta = beta, timing = info_frac, T = trial_duration, minfup = minfup, r = 32, tol = 1e-08,
  lambdaC = fail_rate$fail_rate, eta = fail_rate$dropout_rate, hr = fail_rate$hr,
  sfu = upar$sf, sfupar = upar$param, sfl = lpar$sf, sflpar = lpar$param
)
asymmetricSafetyBinding |> gsBoundSummary()
#>     Analysis              Value Efficacy Futility
#>    IA 1: 35%                  Z   3.6128  -0.7271
#>       N: 356        p (1-sided)   0.0002   0.7664
#>  Events: 101       ~HR at bound   0.4856   1.1565
#>    Month: 14   P(Cross) if HR=1   0.0002   0.2336
#>              P(Cross) if HR=0.7   0.0340   0.0060
#>    IA 2: 70%                  Z   2.4405  -0.4203
#>       N: 394        p (1-sided)   0.0073   0.6629
#>  Events: 201       ~HR at bound   0.7082   1.0612
#>    Month: 23   P(Cross) if HR=1   0.0074   0.3982
#>              P(Cross) if HR=0.7   0.5353   0.0070
#>        Final                  Z   1.9979  -0.2531
#>       N: 394        p (1-sided)   0.0229   0.5999
#>  Events: 286       ~HR at bound   0.7895   1.0304
#>    Month: 36   P(Cross) if HR=1   0.0250   0.5000
#>              P(Cross) if HR=0.7   0.8500   0.0072

Example 7: Alternate bound types

We consider two types of alternative boundary computation approaches.

  • Computing futility bounds based on a hazard ratio.
  • Computing efficacy bounds with a Haybittle-Peto or a related Fleming-Harrington-O’Brien approach.

We begin with a futility bound. We will consider a non-binding futility bound as it does not impact the efficacy bound. Assume the clinical trial team wishes to stop the trial at the first two interim analyses if a targeted interim hazard ratio is not achieved. This approach can require a bit of iteration (trial and error) to incorporate the final design endpoint count; we skip over this iteration here. We assume we wish to consider stopping for futility if a hazard ratio greater than 1 and 0.9 are observed at interim analyses 1 and 2 with 104 and 209 events observed, respectively. The final analysis is planned for 300 events.

# Targeted events at interim and final analysis
# This is based on above designs and then adjusted, as necessary
targeted_events <- c(104, 209, 300)

We wish to translate the hazard ratios specified to corresponding Z-values; this can be done as follows.

interim_futility_z <- -gsDesign::hrn2z(hr = c(1, .9), n = targeted_events[1:2])
interim_futility_z
#> [1] 0.0000000 0.7615897

We will add a final futility bound of -Inf, indicating no final futility analysis; this gives us a vector of Z-value bounds for all analyses. For this type of bound, Type II error will be computed rather based on bounds rather than the spending approach were bounds are computed based on specified spending.

lower <- gs_b
# Allows specifying fixed Z-values for futility
# Translated HR bounds to Z-value scale
lpar <- c(interim_futility_z, -Inf)

For the efficacy bound, we first consider a Haybittle-Peto fixed bound for interim analyses. Using a Bonferroni approach, we test at nominal levels 0.001, 0.001, and 0.023 at the 3 analyses. By not accounting for correlations, this will actually not quite use all of the 0.025 1-sided Type I error allowed. We allow the user to substitute this code for what follows to verify this.

upper <- gs_b
upar <- qnorm(c(.001, .001, .0023), lower.tail = FALSE)

The alternative approach is to use a fixed spending approach at each analysis as suggested by Fleming, Harrington, and O’Brien (1984). Again, with some iteration not shown, we use a piecewise linear spending function to select interim bounds that match the desired Haybittle-Peto interim bounds. However, using this approach a slightly more liberal final bound is achieved that still controls Type I error.

upper <- gs_spending_bound
upar <- list(
  sf = gsDesign::sfLinear,
  total_spend = alpha,
  param = c(targeted_events[1:2] / targeted_events[3], c(.001, .0018) / .025),
  timing = NULL
)

asymmetric_fixed_bounds <- gs_design_ahr(
  enroll_rate = enroll_rate, fail_rate = fail_rate,
  ratio = ratio, beta = beta,
  # Information fraction at analyses and trial duration
  info_frac = info_frac, analysis_time = trial_duration,
  # Precision parameters for computations
  r = 32, tol = 1e-08,
  # Use NULL information for Type I error, H1 information for Type II error
  info_scale = "h0_info",
  # Function and parameter(s) for upper spending bound
  upper = upper, upar = upar,
  lower = lower, lpar = lpar,
  # Non-binding futility bounds
  binding = FALSE
) |> to_integer()

asymmetric_fixed_bounds |>
  summary() |>
  gsDesign2::as_gt(
    title = "2-sided asymmetric safety design with fixed non-binding futility",
    subtitle = "Futility bounds computed to approximate HR"
  ) |>
  gt::tab_footnote(footnote = "Integer-based sample size and event counts")
2-sided asymmetric safety design with fixed non-binding futility
Futility bounds computed to approximate HR
Bound Z Nominal p1 ~HR at bound2 Cumulative boundary crossing probability
Alternate hypothesis Null hypothesis
Analysis: 1 Time: 14.4 N: 371.6 Event: 104 AHR: 0.7 Information fraction: 0.35
Futility 0.00 0.5000 1.0000 0.0345 0.5000
Efficacy 3.09 0.0010 0.5455 0.1018 0.0010
Analysis: 2 Time: 23.1 N: 414 Event: 209 AHR: 0.7 Information fraction: 0.7
Futility 0.76 0.2232 0.9000 0.0566 0.8019
Efficacy 3.10 0.0010 0.6510 0.3181 0.0018
Analysis: 3 Time: 35.8 N: 414 Event: 300 AHR: 0.7 Information fraction: 1
Efficacy 1.97 0.0244 0.7966 0.8520 3 0.0234
Integer-based sample size and event counts
1 One-sided p-value for experimental vs control treatment. Value < 0.5 favors experimental, > 0.5 favors control.
2 Approximate hazard ratio to cross bound.
3 Cumulative alpha for final analysis (0.0234) is less than the full alpha (0.025) when the futility bound is non-binding. The smaller value subtracts the probability of crossing a futility bound before crossing an efficacy bound at a later analysis (0.025 - 0.0016 = 0.0234) under the null hypothesis.

We see that the targeted bounds are achieved with nominal p-values of 0.0001 for each interim efficacy bound and the targeted hazard ratios at interim futility bounds. With these methods, trial designers have more control over design characteristics they may desire. In particular, we note that the Haybittle-Peto efficacy bounds are less stringent at the first interim and more stringent at the second interim than corresponding O’Brien-Fleming-like bounds we computed with the spending approach. This may or may not be desirable.

References

Fleming, Thomas R, David P Harrington, and Peter C O’Brien. 1984. “Designs for Group Sequential Tests.” Controlled Clinical Trials 5 (4): 348–61.