Overview

We provide simple examples for use of the gsDesign2 package for deriving fixed and group sequential designs under non-proportional hazards. The piecewise model for enrollment, failure rates, dropout rates and changing hazard ratio over time allow great flexibility in design assumptions. Users are encouraged to suggest features that would be of immediate and long-term interest to add.

Topics included here are:

• Packages required and how they are used.
• Specifying enrollment rates.
• Specifying failure and dropout rates with possibly changing hazard ratio over time.
• Deriving a fixed design with no interim analysis.
• Simple boundary specification for group sequential design.
• Deriving a group sequential design under non-proportional hazards.
• Displaying design properties.
• Design properties under alternate assumptions.
• Differences from gsDesign.
• Future enhancement priorities.

All of these items are discussed briefly to enable a quick start for early adopters while also suggesting the ultimate possibilities that the software enables. Finally, while the final section provides current enhancement priorities, potential topic-related enhancements are discussed throughout the document.

Packages used

• The gsDesign package is used as a check for results under proportional hazards as well as a source from deriving bounds using spending functions.
• The gsDesign2 package provides
  • computations to compute expected event accumulation and average hazard ratio over time; these are key inputs to the group sequential distribution parameters.
  • implement group sequential distribution theory under non-proportional hazards and to derive a wide variety of boundary types for group sequential designs.
• The simtrial package is used to verify design properties using simulation.

library(gsDesign)
library(gsDesign2)
library(knitr)
library(dplyr)
library(gt)
library(ggplot2)

Enrollment rates

Piecewise constant enrollment rates are input in a tabular format. Here we assume enrollment will ramp-up with 25%, 50%, and 75% of the final enrollment rate for 2 months each followed by a steady state 100% enrollment for another 6 months. The rates will be increased later to power the design appropriately. However, the fixed enrollment rate periods will remain unchanged.

enroll_rate <- define_enroll_rate(
  duration = c(2, 2, 2, 6),
  rate = (1:4) / 4
)

enroll_rate %>% gt()

Failure and dropout rates

Constant failure and dropout rates are specified by study period and stratum; we consider a single stratum here. A hazard ratio is provided for treatment/control hazard rate for each period and stratum. The dropout rate for each period is assumed the same for each treatment group; this restriction could be eliminated in a future version, if needed. Generally, we take advantage of the identity for an exponential distribution with median m, the corresponding failure rate λ is

λ = log (2)/m.

We consider a control group exponential time-to-event with a 12 month median. We assume a hazard ratio of 1 for 4 months, followed by a hazard ratio of 0.6 thereafter. Finally, we assume a low 0.001 exponential dropout rate per month for both treatment groups.

median_surv <- 12

fail_rate <- define_fail_rate(
  duration = c(4, Inf),
  fail_rate = log(2) / median_surv,
  hr = c(1, .6),
  dropout_rate = .001
)

fail_rate %>% gt()

Fixed design

Under the above enrollment, failure and dropout rate assumptions we now derive sample size for a trial targeted to complete in 36 months with no interim analysis, 90% power and 2.5% Type I error.

alpha <- .025
beta <- .1 # 1 - targeted power
d <- fixed_design_ahr(
  enroll_rate = enroll_rate, # Relative enrollment rates
  fail_rate = fail_rate, # Failure rates from above
  alpha = alpha, # Type I error
  power = 1 - beta, # Type II error = 1 - power
  study_duration = 36 # Planned trial duration
)

A quick summary of the targeted sample size is obtained below. Note that you would normally round up N up to an even number and Events to the next integer.

d %>%
  summary() %>%
  as_gt()

The enrollment rates for each period have been increased proportionately to size the trial for the desired properties; the duration for each enrollment rate has not changed.

d$enroll_rate %>% gt()

Group sequential design

We will not go into detail for group sequential designs here. In brief, however, a sequence of tests Z₁, Z₂, …, Z_K that follow a multivariate normal distribution are performed to test if a new treatment is better than control (Jennison and Turnbull (1999)). We assume Z_k > 0 is favorable for the experimental treatment. Generally Type I error for this set of tests will be controlled under the null hypothesis of no treatment difference by a sequence of bounds b₁, b₂, …, b_K such that for a chosen Type I error α > 0 we have

α = 1 − P₀(∩_k = 1^KZ_k < b_k) Where P₀() refers to a probability under the null hypothesis. This is referred to as a non-binding bound since it is assumed the trial will not be stopped early for futility if some Z_k is small.

design1s <- gs_design_ahr(
  alpha = alpha,
  beta = beta,
  enroll_rate = enroll_rate,
  fail_rate = fail_rate,
  analysis_time = c(16, 26, 36), # Calendar time of planned analyses
  upper = gs_spending_bound, # Spending function bound for efficacy
  upar = list(sf = gsDesign::sfLDOF, total_spend = 0.025), # Specify spending function and total Type I error
  lower = gs_b, lpar = rep(-Inf, 3), # No futility bound
  info_scale = "h0_h1_info"
)

design1s %>%
  summary() %>%
  as_gt(
    title = "1-sided group sequential bound using AHR method",
    subtitle = "Lan-DeMets spending to approximate O'Brien-Fleming bound"
  )

x <- gsDesign(k = 3, test.type = 1, timing = design1s$analysis$info_frac, sfu = sfLDOF)
cat(
  "gsDesign\n  Upper bound: ", x$upper$bound,
  "\n  Cumulative boundary crossing probability (H0): ", cumsum(x$upper$prob[, 1]),
  "\n  Timing (IF): ", x$timing,
  "\ngs_design_ahr\n  Upper bound: ", design1s$bound$z,
  "\n  Cumulative boundary crossing probability (H0): ", design1s$bound$probability0,
  "\n  Timinng (IF): ", design1s$analysis$info_frac,
  "\n"
)
#> gsDesign
#>   Upper bound:  3.013804 2.264946 2.027236 
#>   Cumulative boundary crossing probability (H0):  0.00128997 0.01217731 0.025 
#>   Timing (IF):  0.4850799 0.7993622 1 
#> gs_design_ahr
#>   Upper bound:  3.003506 2.256138 2.028823 
#>   Cumulative boundary crossing probability (H0):  0.001334442 0.01246455 0.025 
#>   Timinng (IF):  0.4850799 0.7993622 1

design2ss <- gs_design_ahr(
  alpha = alpha,
  beta = beta,
  enroll_rate = enroll_rate,
  fail_rate = fail_rate,
  analysis_time = c(16, 26, 36), #  Calendar analysis times
  upper = gs_spending_bound,
  upar = list(sf = gsDesign::sfLDOF, total_spend = 0.025),
  lower = gs_spending_bound,
  lpar = list(sf = gsDesign::sfLDOF, total_spend = 0.025),
  h1_spending = FALSE # This specifies futility testing with spending under NULL
)

design2ss %>%
  summary() %>%
  as_gt(
    title = "2-sided symmetric group sequential bound using AHR method",
    subtitle = "Lan-DeMets spending to approximate O'Brien-Fleming bound"
  )

ggplot(
  data = design2ss$analysis %>% left_join(design2ss$bound, by = "analysis"),
  aes(x = event, y = z, group = bound)
) +
  geom_line(aes(linetype = bound)) +
  geom_point() +
  ggtitle("2-sided symmetric bounds with O'Brien-Fleming-like spending")

design2sa <- gs_design_ahr(
  alpha = alpha,
  beta = beta,
  enroll_rate = enroll_rate,
  fail_rate = fail_rate,
  analysis_time = c(12, 16, 26, 36),
  upper = gs_spending_bound,
  upar = list(sf = gsDesign::sfLDOF, total_spend = 0.025), # Same efficacy bound as before
  test_lower = c(FALSE, TRUE, TRUE, TRUE), # Only test efficacy after IA1
  lower = gs_b,
  lpar = c(rep(qnorm(.05), 2), -Inf, -Inf) # Fixed lower bound at first 2 analyses
)

design2sa %>%
  summary() %>%
  as_gt(
    title = "2-sided asymmetric group sequential bound using AHR method",
    subtitle = "Lan-DeMets spending to approximate O'Brien-Fleming bound
    for efficacy, futility disaster check at IA1, IA2 only"
  )