Group sequential design using weighted log-rank test under non-proportional hazards
Source:R/gs_design_wlr.r
gs_design_wlr.RdGroup sequential design using weighted log-rank test under non-proportional hazards
Usage
gs_design_wlr(
enrollRates = tibble::tibble(Stratum = "All", duration = c(2, 2, 10), rate = c(3, 6,
9)),
failRates = tibble::tibble(Stratum = "All", duration = c(3, 100), failRate =
log(2)/c(9, 18), hr = c(0.9, 0.6), dropoutRate = rep(0.001, 2)),
ratio = 1,
weight = wlr_weight_fh,
approx = "asymptotic",
alpha = 0.025,
beta = 0.1,
IF = NULL,
analysisTimes = 36,
binding = FALSE,
upper = gs_b,
upar = gsDesign(k = 3, test.type = 1, n.I = c(0.25, 0.75, 1), sfu = sfLDOF, sfupar =
NULL)$upper$bound,
lower = gs_b,
lpar = c(qnorm(0.1), -Inf, -Inf),
h1_spending = TRUE,
test_upper = TRUE,
test_lower = TRUE,
r = 18,
tol = 1e-06
)Arguments
- enrollRates
enrollment rates
- failRates
failure and dropout rates
- ratio
Experimental:Control randomization ratio (not yet implemented)
- weight
weight of weighted log rank test
"1"=unweighted,"n"=Gehan-Breslow,"sqrtN"=Tarone-Ware,"FH_p[a]_q[b]"= Fleming-Harrington with p=a and q=b
- approx
approximate estimation method for Z statistics
"event driven"= only work under proportional hazard model with log rank test"asymptotic"
- alpha
One-sided Type I error
- beta
Type II error
- IF
Targeted information fraction at each analysis
- analysisTimes
Minimum time of analysis
- binding
indicator of whether futility bound is binding; default of FALSE is recommended
- upper
Function to compute upper bound
- upar
Parameter passed to
upper()- lower
Function to compute lower bound
- lpar
Parameter passed to
lower()- h1_spending
Indicator that lower bound to be set by spending under alternate hypothesis (input
failRates) if spending is used for lower bound- test_upper
indicator of which analyses should include an upper (efficacy) bound; single value of TRUE (default) indicates all analyses; otherwise, a logical vector of the same length as
infoshould indicate which analyses will have an efficacy bound- test_lower
indicator of which analyses should include an lower bound; single value of TRUE (default) indicates all analyses; single value FALSE indicated no lower bound; otherwise, a logical vector of the same length as
infoshould indicate which analyses will have a lower bound- r
Integer, at least 2; default of 18 recommended by Jennison and Turnbull
- tol
Tolerance parameter for boundary convergence (on Z-scale)
Examples
library(dplyr)
library(mvtnorm)
library(gsDesign)
enrollRates <- tibble::tibble(Stratum = "All", duration = 12, rate = 500/12)
failRates <- tibble::tibble(Stratum = "All",
duration = c(4, 100),
failRate = log(2) / 15, # median survival 15 month
hr = c(1, .6),
dropoutRate = 0.001)
x <- gsDesign::gsSurv( k = 3 , test.type = 4 , alpha = 0.025 ,
beta = 0.2 , astar = 0 , timing = c( 1 ) ,
sfu = sfLDOF , sfupar = c( 0 ) , sfl = sfLDOF ,
sflpar = c( 0 ) , lambdaC = c( 0.1 ) ,
hr = 0.6 , hr0 = 1 , eta = 0.01 ,
gamma = c( 10 ) ,
R = c( 12 ) , S = NULL ,
T = 36 , minfup = 24 , ratio = 1 )
# User defined boundary
gs_design_wlr(enrollRates = enrollRates, failRates = failRates,
ratio = 1, alpha = 0.025, beta = 0.2,
weight = function(x, arm0, arm1){
gsdmvn:::wlr_weight_fh(x, arm0, arm1, rho = 0, gamma = 0.5)
},
upar = x$upper$bound,
lpar = x$lower$bound,
analysisTimes = c(12, 24, 36))
#> $enrollRates
#> # A tibble: 1 × 3
#> Stratum duration rate
#> <chr> <dbl> <dbl>
#> 1 All 12 30.6
#>
#> $failRates
#> # A tibble: 2 × 5
#> Stratum duration failRate hr dropoutRate
#> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 All 4 0.0462 1 0.001
#> 2 All 100 0.0462 0.6 0.001
#>
#> $bounds
#> # A tibble: 6 × 11
#> Analysis Bound Time N Events Z Probability AHR theta info info0
#> <int> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1 Upper 12 368. 79.0 3.71 0.00356 0.781 0.626 2.65 2.66
#> 2 2 Upper 24 368. 181. 2.51 0.504 0.666 0.765 11.3 11.6
#> 3 3 Upper 36 368. 244. 1.99 0.800 0.639 0.732 20.0 20.9
#> 4 1 Lower 12 368. 79.0 -0.236 0.105 0.781 0.626 2.65 2.66
#> 5 2 Lower 24 368. 181. 1.17 0.157 0.666 0.765 11.3 11.6
#> 6 3 Lower 36 368. 244. 1.99 0.200 0.639 0.732 20.0 20.9
#>
# Boundary derived by spending function
gs_design_wlr(enrollRates = enrollRates, failRates = failRates,
ratio = 1, alpha = 0.025, beta = 0.2,
weight = function(x, arm0, arm1){
gsdmvn:::wlr_weight_fh(x, arm0, arm1, rho = 0, gamma = 0.5)
},
upper = gs_spending_bound,
upar = list(sf = gsDesign::sfLDOF, total_spend = 0.025),
lower = gs_spending_bound,
lpar = list(sf = gsDesign::sfLDOF, total_spend = 0.2),
analysisTimes = c(12, 24, 36))
#> $enrollRates
#> # A tibble: 1 × 3
#> Stratum duration rate
#> <chr> <dbl> <dbl>
#> 1 All 12 24.0
#>
#> $failRates
#> # A tibble: 2 × 5
#> Stratum duration failRate hr dropoutRate
#> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 All 4 0.0462 1 0.001
#> 2 All 100 0.0462 0.6 0.001
#>
#> $bounds
#> # A tibble: 6 × 11
#> Analysis Bound Time N Events Z Probability AHR theta info info0
#> <int> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1 Upper 12 288. 61.9 6.18 0.0000000671 0.781 0.626 2.08 2.09
#> 2 2 Upper 24 288. 142. 2.80 0.301 0.666 0.765 8.86 9.07
#> 3 3 Upper 36 288. 191. 1.97 0.800 0.639 0.732 15.7 16.4
#> 4 1 Lower 12 288. 61.9 -2.43 0.000431 0.781 0.626 2.08 2.09
#> 5 2 Lower 24 288. 142. 0.925 0.0882 0.666 0.765 8.86 9.07
#> 6 3 Lower 36 288. 191. 1.97 0.2 0.639 0.732 15.7 16.4
#>