Power for delayed effect scenarios
Keaven M. Anderson
Source:vignettes/articles/story-compare-power-delay-effect.Rmd
story-compare-power-delay-effect.RmdOverview
We consider a delayed effect scenario where
- The control group time-to-event distribution is exponential with a median of 15 months.
- The experimental group has a hazard ratio vs. control of 1 for 6 months and 0.6 thereafter.
- Enrollment at a constant rate for 12 months.
- Total study duration from 20 to 48 months.
- Exponential dropout rate of 0.001 per month.
enroll_rate <- define_enroll_rate(duration = 12, rate = 1)
fail_rate <- define_fail_rate(
duration = c(6, 100),
fail_rate = log(2) / 15,
hr = c(1, .6),
dropout_rate = 0.001
)
enroll_rate %>%
gt() %>%
tab_header(title = "Enrollment Table of Scenario 1")| Enrollment Table of Scenario 1 | ||
| stratum | duration | rate |
|---|---|---|
| All | 12 | 1 |
fail_rate %>%
gt() %>%
tab_header(title = "Failure Table of Scenario 1")| Failure Table of Scenario 1 | ||||
| stratum | duration | fail_rate | dropout_rate | hr |
|---|---|---|---|---|
| All | 6 | 0.04620981 | 0.001 | 1.0 |
| All | 100 | 0.04620981 | 0.001 | 0.6 |
For the above scenarios, we investigate the power, sample size and events under 6 tests:
-
fh_05: The Fleming-Harrington with \rho=0, \gamma=0.5 test to obtain power of 85% given 1-sided Type I error of 0.025. -
fh_00: The regular logrank test with \rho=0, \gamma=0 under fixed study duration \in\{20, 24, 28, \ldots, 60\}. -
mc2_test: The MaxCombo test including 2 WLR tests, i.e., \{(\rho=0, \gamma=0, \tau = -1), (\rho=0, \gamma=0.5, \tau = -1)\}. -
mc2_test: The MaxCombo test including 3 WLR tests, i.e., \{(\rho=0, \gamma=0, \tau = -1), (\rho=0, \gamma=0.5, \tau = -1), (\rho=0.5, \gamma=0.5, \tau = -1)\}. -
mc4_test: The MaxCombo test including 4 WLR tests, i.e., \{(\rho=0, \gamma=0, \tau = -1), (\rho=0, \gamma=0.5, \tau = -1), (\rho=0.5, \gamma=0.5, \tau = -1), (\rho=0.5, \gamma=0, \tau = -1)\}. -
mb_6: The Magirr-Burman with \rho=-1, \gamma=0, \tau = 6 test with fixed study duration \in\{20, 24, 28, \ldots, 60\}.
We then compute power for the logrank test. The general summary is that the Fleming-Harrington test has a meaningful power gain relative to logrank regardless of the study durations evaluated.
tab <- NULL
for (trial_duration in seq(24, 60, 4)) {
# Fleming-Harrington rho=0, gamma=0.5 test
fh_05 <- gs_design_wlr(
enroll_rate = enroll_rate,
fail_rate = fail_rate,
ratio = 1,
alpha = 0.025, beta = 0.15,
weight = function(x, arm0, arm1) {
wlr_weight_fh(x, arm0, arm1, rho = 0, gamma = 0.5)
},
upper = gs_b,
lower = gs_b,
upar = qnorm(.975),
lpar = -Inf,
analysis_time = trial_duration
) |> to_integer()
# Regular logrank test
fh_00 <- gs_power_wlr(
enroll_rate = fh_05$enroll_rate,
fail_rate = fail_rate,
ratio = 1,
weight = function(x, arm0, arm1) {
wlr_weight_fh(x, arm0, arm1, rho = 0, gamma = 0)
},
upper = gs_b,
lower = gs_b,
upar = qnorm(.975),
lpar = -Inf,
analysis_time = trial_duration,
event = .1
)
# MaxCombo test 1
mc2_test <- data.frame(
rho = 0, gamma = c(0, .5), tau = -1,
test = 1:2, analysis = 1, analysis_time = trial_duration
)
mc_2 <- gs_power_combo(
enroll_rate = fh_05$enroll_rate,
fail_rate = fail_rate,
fh_test = mc2_test,
upper = gs_spending_combo,
upar = list(sf = gsDesign::sfLDOF, total_spend = 0.025),
lower = gs_spending_combo,
lpar = list(sf = gsDesign::sfLDOF, total_spend = 0.01)
)
# MaxCombo test 2
mc3_test <- data.frame(
rho = c(0, 0, .5), gamma = c(0, .5, .5), tau = -1,
test = 1:3, analysis = 1, analysis_time = trial_duration
)
mc_3 <- gs_power_combo(
enroll_rate = fh_05$enroll_rate,
fail_rate = fail_rate,
fh_test = mc3_test,
upper = gs_spending_combo,
upar = list(sf = gsDesign::sfLDOF, total_spend = 0.025),
lower = gs_spending_combo,
lpar = list(sf = gsDesign::sfLDOF, total_spend = 0.01)
)
# MaxCombo test
mc4_test <- data.frame(
rho = c(0, 0, .5, .5), gamma = c(0, .5, .5, 0), tau = -1,
test = 1:4, analysis = 1, analysis_time = trial_duration
)
mc_4 <- gs_power_combo(
enroll_rate = fh_05$enroll_rate,
fail_rate = fail_rate,
fh_test = mc4_test,
upper = gs_spending_combo,
upar = list(sf = gsDesign::sfLDOF, total_spend = 0.025),
lower = gs_spending_combo,
lpar = list(sf = gsDesign::sfLDOF, total_spend = 0.01)
)
# Magirr-Burman rho=-1, gamma=0, tau = 6 test
mb_6 <- gs_power_wlr(
enroll_rate = fh_05$enroll_rate,
fail_rate = fail_rate,
ratio = 1,
weight = function(x, arm0, arm1) {
wlr_weight_fh(x, arm0, arm1, rho = -1, gamma = 0, tau = 15)
},
upper = gs_b,
lower = gs_b,
upar = qnorm(.975),
lpar = -Inf,
analysis_time = trial_duration,
event = .1
)
tab_new <- tibble(
`Study duration` = trial_duration,
N = fh_05$analysis$n[1],
Events = fh_05$analysi$event[1],
`Events/N` = Events / N,
# We use the AHR from regular WLR as the AHR of different MaxCombo test
AHR = as.numeric(fh_00$analysis$ahr[1]),
`FH(0, 0.5) power` = fh_05$bound$probability[1],
`FH(0, 0) power` = fh_00$bound$probability[1],
`MC2 power` = mc_2$bound$probability[1],
`MC4 power` = mc_4$bound$probability[1],
`MC3 power` = mc_3$bound$probability[1],
`MB6 power` = mb_6$bound$probability[1]
)
tab <- rbind(tab, tab_new)
}
tab %>%
gt() %>%
fmt_number(columns = c(2, 3), decimals = 1) %>%
fmt_number(columns = 4, decimals = 2) %>%
fmt_number(columns = 5, decimals = 4) %>%
fmt_number(columns = 6:11, decimals = 2)| Study duration | N | Events | Events/N | AHR | FH(0, 0.5) power | FH(0, 0) power | MC2 power | MC4 power | MC3 power | MB6 power |
|---|---|---|---|---|---|---|---|---|---|---|
| 24 | 696.0 | 350.0 | 0.50 | 0.7688 | 0.85 | 0.69 | 0.82 | 0.81 | 0.82 | 0.80 |
| 28 | 522.0 | 297.0 | 0.57 | 0.7473 | 0.85 | 0.71 | 0.82 | 0.81 | 0.82 | 0.81 |
| 32 | 428.0 | 267.0 | 0.62 | 0.7325 | 0.85 | 0.72 | 0.82 | 0.81 | 0.82 | 0.82 |
| 36 | 370.0 | 248.0 | 0.67 | 0.7218 | 0.85 | 0.73 | 0.82 | 0.81 | 0.82 | 0.82 |
| 40 | 332.0 | 235.0 | 0.71 | 0.7138 | 0.85 | 0.74 | 0.82 | 0.81 | 0.82 | 0.82 |
| 44 | 304.0 | 226.0 | 0.74 | 0.7076 | 0.85 | 0.74 | 0.82 | 0.81 | 0.82 | 0.82 |
| 48 | 282.0 | 219.0 | 0.78 | 0.7027 | 0.85 | 0.74 | 0.81 | 0.80 | 0.82 | 0.82 |
| 52 | 266.0 | 213.0 | 0.80 | 0.6987 | 0.85 | 0.75 | 0.81 | 0.80 | 0.81 | 0.82 |
| 56 | 254.0 | 209.0 | 0.82 | 0.6955 | 0.85 | 0.75 | 0.81 | 0.80 | 0.81 | 0.82 |
| 60 | 244.0 | 206.0 | 0.84 | 0.6929 | 0.85 | 0.75 | 0.81 | 0.80 | 0.81 | 0.82 |
An Alternative Scenario
Now we consider an alternate scenario where the placebo group starts with the same median, but then has a piecewise change to a median of 30 after 16 months and with a hazard ratio of 0.85 during that late period.
enroll_rate <- define_enroll_rate(duration = 12, rate = 1)
fail_rate <- define_fail_rate(
duration = c(6, 10, 100),
# In Scenario 1: fail_rate = log(2) / 15,
fail_rate = log(2) / c(15, 15, 30),
dropout_rate = 0.001,
# In Scenario 1: hr = c(1, .6)
hr = c(1, .6, .85)
)
enroll_rate %>%
gt() %>%
tab_header(title = "Enrollment Table of Scenario 2")| Enrollment Table of Scenario 2 | ||
| stratum | duration | rate |
|---|---|---|
| All | 12 | 1 |
fail_rate %>%
gt() %>%
tab_header(title = "Failure Table of Scenario 2")| Failure Table of Scenario 2 | ||||
| stratum | duration | fail_rate | dropout_rate | hr |
|---|---|---|---|---|
| All | 6 | 0.04620981 | 0.001 | 1.00 |
| All | 10 | 0.04620981 | 0.001 | 0.60 |
| All | 100 | 0.02310491 | 0.001 | 0.85 |
tab <- NULL
for (trial_duration in seq(20, 60, 4)) {
# Fleming-Harrington rho=0, gamma=0.5 test
fh_05 <- gs_design_wlr(
enroll_rate = enroll_rate,
fail_rate = fail_rate,
ratio = 1,
alpha = 0.025, beta = 0.15,
weight = function(x, arm0, arm1) {
wlr_weight_fh(x, arm0, arm1, rho = 0, gamma = 0.5)
},
upper = gs_b,
upar = qnorm(.975),
lower = gs_b,
lpar = -Inf,
analysis_time = trial_duration
) |> to_integer()
# Regular logrank test
fh_00 <- gs_power_wlr(
enroll_rate = fh_05$enroll_rate,
fail_rate = fail_rate,
ratio = 1,
weight = function(x, arm0, arm1) {
wlr_weight_fh(x, arm0, arm1, rho = 0, gamma = 0)
},
upper = gs_b,
upar = qnorm(.975),
lower = gs_b,
lpar = -Inf,
analysis_time = trial_duration,
event = .1
)
# MaxCombo test
mc2_test <- data.frame(
rho = 0, gamma = c(0, .5), tau = -1,
test = 1:2, analysis = 1, analysis_time = trial_duration
)
mc_2 <- gs_power_combo(
enroll_rate = fh_05$enroll_rate,
fail_rate = fail_rate,
fh_test = mc2_test,
upper = gs_spending_combo,
upar = list(sf = gsDesign::sfLDOF, total_spend = 0.025),
lower = gs_spending_combo,
lpar = list(sf = gsDesign::sfLDOF, total_spend = 0.01)
)
# MaxCombo test
mc3_test <- data.frame(
rho = c(0, 0, .5), gamma = c(0, .5, .5), tau = -1,
test = 1:3, analysis = 1, analysis_time = trial_duration
)
mc_3 <- gs_power_combo(
enroll_rate = fh_05$enroll_rate,
fail_rate = fail_rate,
fh_test = mc3_test,
upper = gs_spending_combo,
upar = list(sf = gsDesign::sfLDOF, total_spend = 0.025),
lower = gs_spending_combo,
lpar = list(sf = gsDesign::sfLDOF, total_spend = 0.01)
)
# MaxCombo test
mc4_test <- data.frame(
rho = c(0, 0, .5, .5), gamma = c(0, .5, .5, 0), tau = -1,
test = 1:4, analysis = 1, analysis_time = trial_duration
)
mc_4 <- gs_power_combo(
enroll_rate = fh_05$enroll_rate,
fail_rate = fail_rate, fh_test = mc4_test,
upper = gs_spending_combo,
upar = list(sf = gsDesign::sfLDOF, total_spend = 0.025),
lower = gs_spending_combo,
lpar = list(sf = gsDesign::sfLDOF, total_spend = 0.01)
)
# Magirr-Burman rho=-1, gamma=0, tau = 6 test
mb_6 <- gs_power_wlr(
enroll_rate = fh_05$enroll_rate,
fail_rate = fail_rate,
ratio = 1,
weight = function(x, arm0, arm1) {
wlr_weight_fh(x, arm0, arm1, rho = -1, gamma = 0, tau = 15)
},
upper = gs_b,
lower = gs_b,
upar = qnorm(.975),
lpar = -Inf,
analysis_time = trial_duration,
event = .1
)
tab_new <- tibble(
`Study duration` = trial_duration,
N = fh_05$analysis$n[1],
Events = fh_05$analysi$event[1],
`Events/N` = Events / N,
# We use the AHR from regular WLR as the AHR of different MaxCombo test
AHR = as.numeric(fh_00$analysis$ahr[1]),
`FH(0, 0.5) power` = fh_05$bound$probability[1],
`FH(0, 0) power` = fh_00$bound$probability[1],
`MC2 power` = mc_2$bound$probability[1],
`MC4 power` = mc_4$bound$probability[1],
`MC3 power` = mc_3$bound$probability[1],
`MB6 power` = mb_6$bound$probability[1]
)
tab <- rbind(tab, tab_new)
}
tab %>%
gt() %>%
fmt_number(columns = c(2, 3), decimals = 1) %>%
fmt_number(columns = 4, decimals = 2) %>%
fmt_number(columns = 5, decimals = 4) %>%
fmt_number(columns = 6:11, decimals = 2)| Study duration | N | Events | Events/N | AHR | FH(0, 0.5) power | FH(0, 0) power | MC2 power | MC4 power | MC3 power | MB6 power |
|---|---|---|---|---|---|---|---|---|---|---|
| 20 | 1,226.0 | 518.0 | 0.42 | 0.8104 | 0.85 | 0.67 | 0.82 | 0.81 | 0.82 | 0.79 |
| 24 | 928.0 | 450.0 | 0.48 | 0.7912 | 0.85 | 0.70 | 0.82 | 0.81 | 0.82 | 0.80 |
| 28 | 876.0 | 466.0 | 0.53 | 0.7892 | 0.85 | 0.72 | 0.82 | 0.82 | 0.82 | 0.81 |
| 32 | 894.0 | 507.0 | 0.57 | 0.7929 | 0.85 | 0.74 | 0.83 | 0.82 | 0.83 | 0.82 |
| 36 | 904.0 | 544.0 | 0.60 | 0.7960 | 0.85 | 0.76 | 0.83 | 0.82 | 0.83 | 0.83 |
| 40 | 912.0 | 577.0 | 0.63 | 0.7984 | 0.85 | 0.77 | 0.83 | 0.83 | 0.84 | 0.83 |
| 44 | 916.0 | 605.0 | 0.66 | 0.8005 | 0.85 | 0.78 | 0.83 | 0.83 | 0.84 | 0.84 |
| 48 | 918.0 | 630.0 | 0.69 | 0.8022 | 0.85 | 0.79 | 0.83 | 0.84 | 0.84 | 0.84 |
| 52 | 918.0 | 652.0 | 0.71 | 0.8037 | 0.85 | 0.80 | 0.83 | 0.84 | 0.85 | 0.84 |
| 56 | 920.0 | 672.0 | 0.73 | 0.8050 | 0.85 | 0.80 | 0.84 | 0.84 | 0.85 | 0.85 |
| 60 | 918.0 | 690.0 | 0.75 | 0.8061 | 0.85 | 0.81 | 0.84 | 0.85 | 0.85 | 0.85 |