Segmented vs. Sequential Analyses

segmented_vs_sequential.R

library(segHT)
library(rpact)

looks <- 3
n_seg <- 50
alpha_level <- 0.05 
true_d <- 0.5 # can not enter 0, segmented_hyp_test_outcomes gives error

###############################
# Segmented procedure ----
expected_summary <- segmented_hyp_test_outcomes(
  max_n_segments = looks,
  n_per_segment = n_seg,
  alpha_total = alpha_level,
  alpha_strong = 0.025,
  stat_procedure_name = "2t",
  effect_size = true_d,
  base_rate = 1
)

# Reproduces Figure 2B
expected_summary$pr_reject_by_segment
expected_summary$pr_ftr_by_segment

#######################################################
# Pocock spending function, stopping for futility ----

seq_design <- getDesignGroupSequential(
  kMax = looks,
  typeOfDesign = "asP",
  typeBetaSpending="bsP",
  alpha = alpha_level
)

sample_res <- getPowerMeans(
  design = seq_design,
  alternative = c(0, true_d), 
  stDev = 1, 
  maxNumberOfSubjects = n_seg*looks)


plot(sample_res, type = 6)

# power sequential
sample_res$overallReject
# power segmented
expected_summary$avg_power

# sample size sequential
sample_res$expectedNumberOfSubjects
# sample size segmented
expected_summary$exp_n_subj

##########################################
# Compared to not stopping for futility ----
seq_design <- getDesignGroupSequential(
  kMax = looks,
  typeOfDesign = "asP",
  alpha = alpha_level,
  beta = 0.2
)

sample_res <- getPowerMeans(
  design = seq_design,
  groups = 2,
  alternative = true_d, 
  stDev = 1, 
  allocationRatioPlanned = 1,
  maxNumberOfSubjects = n_seg*looks, 
  normalApproximation = FALSE)

# power sequential
sample_res$overallReject
# power segmented
expected_summary$avg_power

# sample size sequential
sample_res$expectedNumberOfSubjects
# sample size segmented
expected_summary$exp_n_subj