as_rtf: Save a summary table object as an RTF file

Description

Convert and save the summary table object created with as_table to an RTF file using r2rtf; currently only implemented for gsBinomialExact.

Usage

as_rtf(x, ...)
# S3 method for gsBinomialExactTable
as_rtf(
  x,
  file,
  ...,
  title = paste("Operating Characteristics by Underlying Response Rate for",
    "Exact Binomial Group Sequential Design"),
  theta_label = "Underlying Response Rate",
  response_outcome = TRUE,
  bound_label = if (response_outcome) c("Futility Bound", "Efficacy Bound") else
    c("Efficacy Bound", "Futility Bound"),
  en_label = "Expected Sample Sizes",
  prob_decimals = 2,
  en_decimals = 1,
  rr_decimals = 0
)
# S3 method for gsBoundSummary
as_rtf(
  x,
  file,
  ...,
  title = "Boundary Characteristics for Group Sequential Design",
  footnote_p_onesided = "one-side p-value for experimental better than control",
  footnote_appx_effect_at_bound = NULL,
  footnote_p_cross_h0 = "Cumulative type I error assuming binding futility bound",
  footnote_p_cross_h1 = "Cumulative power under the alternate hypothesis",
  footnote_specify = NULL,
  footnote_text = NULL
)

Value

as_rtf() returns the input x invisibly.

Arguments

x: Object to be saved as RTF file.
...: Other parameters that may be specific to the object.
file: File path for the output.
title: Title of the report.
theta_label: Label for theta.
response_outcome: Logical values indicating if the outcome is response rate (TRUE) or failure rate (FALSE). The default value is TRUE.
bound_label: Label for bounds. If the outcome is response rate, then the label is "Futility bound" and "Efficacy bound". If the outcome is failure rate, then the label is "Efficacy bound" and "Futility bound".
en_label: Label for expected number.
prob_decimals: Number of decimal places for probability of crossing.
en_decimals: Number of decimal places for expected number of observations when bound is crossed or when trial ends without crossing.
rr_decimals: Number of decimal places for response rates.
footnote_p_onesided: Footnote for one-side p-value.
footnote_appx_effect_at_bound: Footnote for approximate effect treatment at bound.
footnote_p_cross_h0: Footnote for cumulative type I error.
footnote_p_cross_h1: Footnote for cumulative power under the alternate hypothesis.
footnote_specify: Vector of string to put footnote super script.
footnote_text: Vector of string of footnote text.

Details

Currently only implemented for gsBinomialExact objects. Creates a table to summarize an object. For gsBinomialExact, this summarized operating characteristics across a range of effect sizes.

Examples

Run this code

# as_rtf for gsBinomialExact
zz <- gsBinomialExact(
  k = 3, theta = seq(0.1, 0.9, 0.1), n.I = c(12, 24, 36),
  a = c(-1, 0, 11), b = c(5, 9, 12)
)
zz %>%
  as_table() %>%
  as_rtf(
    file = tempfile(fileext = ".rtf"),
    title = "Power/Type I Error and Expected Sample Size for a Group Sequential Design"
  )

safety_design <- binomialSPRT(
  p0 = .04, p1 = .1, alpha = .04, beta = .2, minn = 4, maxn = 75
)
safety_power <- gsBinomialExact(
  k = length(safety_design$n.I),
  theta = seq(.02, .16, .02),
  n.I = safety_design$n.I,
  a = safety_design$lower$bound,
  b = safety_design$upper$bound
)
safety_power %>%
  as_table() %>%
  as_rtf(
    file = tempfile(fileext = ".rtf"),
    theta_label = "Underlying\nAE Rate",
    prob_decimals = 3,
    bound_label = c("Low Rate", "High Rate")
  )
# as_rtf for gsBoundSummary
xgs <- gsSurv(lambdaC = .2, hr = .5, eta = .1, T = 2, minfup = 1.5)
gsBoundSummary(xgs, timename = "Year", tdigits = 1) %>% as_rtf(file = tempfile(fileext = ".rtf"))

ss <- nSurvival(
  lambda1 = .2, lambda2 = .1, eta = .1, Ts = 2, Tr = .5,
  sided = 1, alpha = .025, ratio = 2
)
xs <- gsDesign(nFixSurv = ss$n, n.fix = ss$nEvents, delta1 = log(ss$lambda2 / ss$lambda1))
gsBoundSummary(xs, logdelta = TRUE, ratio = ss$ratio) %>% as_rtf(file = tempfile(fileext = ".rtf"))

xs <- gsDesign(nFixSurv = ss$n, n.fix = ss$nEvents, delta1 = log(ss$lambda2 / ss$lambda1))
gsBoundSummary(xs, logdelta = TRUE, ratio = ss$ratio) %>% 
  as_rtf(file = tempfile(fileext = ".rtf"),
  footnote_specify = "Z",
  footnote_text = "Z-Score")