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Group sequential design power using weighted log rank test under non-proportional hazards

Source: R/gs_power_wlr.r
gs_power_wlr.Rd

Group sequential design power using weighted log rank test under non-proportional hazards

Usage

gs_power_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",
  events = c(30, 40, 50),
  analysisTimes = NULL,
  binding = FALSE,
  upper = gs_b,
  upar = gsDesign(k = length(events), test.type = 1, n.I = events, maxn.IPlan =
    max(events), sfu = sfLDOF, sfupar = NULL)$upper$bound,
  lower = gs_b,
  lpar = c(qnorm(0.1), rep(-Inf, length(events) - 1)),
  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"

events

Targeted events 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()

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 info should 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 info should 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)

Specification

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