knit_print: Render a `CohortSizeConst` Object

Description

We provide additional utility functions to allow human-friendly rendition of crmPack objects in Markdown and Quarto files

We provide additional utility functions to allow human-friendly rendition of crmPack objects in Markdown and Quarto files. This file contains methods for all design classes, not just those that are direct descendants of Design.

We provide additional utility functions to allow human-friendly rendition of crmPack objects in Markdown and Quarto files

Usage

# S3 method for CohortSizeConst
knit_print(x, ..., asis = TRUE, label = c("participant", "participants"))
# S3 method for CohortSizeRange
knit_print(x, ..., asis = TRUE)
# S3 method for CohortSizeDLT
knit_print(x, ..., tox_label = "toxicity", asis = TRUE)
# S3 method for CohortSizeParts
knit_print(x, ..., asis = TRUE, label = c("participant", "participants"))
# S3 method for CohortSizeMax
knit_print(x, ..., asis = TRUE)
# S3 method for CohortSizeMin
knit_print(x, ..., asis = TRUE)
# S3 method for CohortSizeOrdinal
knit_print(x, ..., tox_label = "toxicity", asis = TRUE)
# S3 method for StartingDose
knit_print(x, ..., asis = TRUE)
# S3 method for RuleDesign
knit_print(x, ..., level = 2L, title = "Design", sections = NA, asis = TRUE)
# S3 method for Design
knit_print(x, ..., level = 2L, title = "Design", sections = NA, asis = TRUE)
# S3 method for DualDesign
knit_print(x, ..., level = 2L, title = "Design", sections = NA, asis = TRUE)
# S3 method for DADesign
knit_print(x, ..., level = 2L, title = "Design", sections = NA, asis = TRUE)
# S3 method for TDDesign
knit_print(x, ..., level = 2L, title = "Design", sections = NA, asis = TRUE)
# S3 method for DualResponsesDesign
knit_print(x, ..., level = 2L, title = "Design", sections = NA, asis = TRUE)
# S3 method for DesignOrdinal
knit_print(x, ..., level = 2L, title = "Design", sections = NA, asis = TRUE)
# S3 method for DesignGrouped
knit_print(
  x,
  ...,
  level = 2L,
  title = "Design",
  sections = c(model = "Dose toxicity model", mono = "Monotherapy rules", combo =
    "Combination therapy rules", other = "Other details"),
  asis = TRUE
)
# S3 method for TDsamplesDesign
knit_print(x, ..., level = 2L, title = "Design", sections = NA, asis = TRUE)
# S3 method for DualResponsesDesign
knit_print(x, ..., level = 2L, title = "Design", sections = NA, asis = TRUE)
# S3 method for DualResponsesSamplesDesign
knit_print(x, ..., level = 2L, title = "Design", sections = NA, asis = TRUE)
# S3 method for RuleDesignOrdinal
knit_print(x, ..., level = 2L, title = "Design", sections = NA, asis = TRUE)
# S3 method for GeneralData
knit_print(
  x,
  ...,
  asis = TRUE,
  label = c("participant", "participants"),
  full_grid = FALSE,
  summarise = c("none", "dose", "cohort"),
  summarize = summarise,
  units = NA,
  format_func = h_knit_format_func
)
# S3 method for DataParts
knit_print(
  x,
  ...,
  asis = TRUE,
  label = c("participant", "participants"),
  full_grid = FALSE,
  summarise = c("none", "dose", "cohort"),
  summarize = summarise,
  units = NA,
  format_func = h_knit_format_func
)
# S3 method for DualEndpoint
knit_print(
  x,
  ...,
  asis = TRUE,
  use_values = TRUE,
  fmt = "%5.2f",
  units = NA,
  tox_label = "toxicity",
  biomarker_label = "PD biomarker"
)
# S3 method for ModelParamsNormal
knit_print(
  x,
  use_values = TRUE,
  fmt = "%5.2f",
  params = c("alpha", "beta"),
  preamble = "The prior for θ is given by\\n",
  asis = TRUE,
  theta = "\\theta",
  ...
)
# S3 method for GeneralModel
knit_print(
  x,
  ...,
  params = c("alpha", "beta"),
  asis = TRUE,
  use_values = TRUE,
  fmt = "%5.2f",
  units = NA
)
# S3 method for LogisticKadane
knit_print(
  x,
  ...,
  asis = TRUE,
  use_values = TRUE,
  fmt = "%5.2f",
  units = NA,
  tox_label = "toxicity"
)
# S3 method for LogisticKadaneBetaGamma
knit_print(
  x,
  ...,
  asis = TRUE,
  use_values = TRUE,
  fmt = "%5.2f",
  tox_label = "toxicity",
  units = NA
)
# S3 method for LogisticLogNormal
knit_print(
  x,
  ...,
  use_values = TRUE,
  fmt = "%5.2f",
  params = c(`\\alpha` = "alpha", `log(\\beta)` = "beta"),
  preamble = "The prior for θ is given by\\n",
  asis = TRUE
)
# S3 method for LogisticLogNormalMixture
knit_print(x, ..., asis = TRUE, use_values = TRUE, fmt = "%5.2f", units = NA)
# S3 method for LogisticLogNormalSub
knit_print(
  x,
  ...,
  use_values = TRUE,
  fmt = "%5.2f",
  params = c(`\\alpha` = "alpha", `log(\\beta)` = "beta"),
  preamble = "The prior for θ is given by\\n",
  asis = TRUE
)
# S3 method for LogisticNormalMixture
knit_print(x, ..., asis = TRUE, use_values = TRUE, fmt = "%5.2f", units = NA)
# S3 method for LogisticNormalFixedMixture
knit_print(x, ..., asis = TRUE, use_values = TRUE, fmt = "%5.2f", units = NA)
# S3 method for OneParLogNormalPrior
knit_print(
  x,
  ...,
  tox_label = "toxicity",
  asis = TRUE,
  use_values = TRUE,
  fmt = "%5.2f"
)
# S3 method for OneParExpPrior
knit_print(x, ..., asis = TRUE)
# S3 method for LogisticLogNormalGrouped
knit_print(
  x,
  ...,
  use_values = TRUE,
  fmt = "%5.2f",
  params = c(`\\alpha` = "alpha", `\\beta` = "beta", `log(\\delta_0)` = "delta_0",
    `log(\\delta_1)` = "delta_1"),
  preamble = "The prior for θ is given by\\n",
  asis = TRUE
)
# S3 method for LogisticLogNormalOrdinal
knit_print(
  x,
  ...,
  use_values = TRUE,
  fmt = "%5.2f",
  params = NA,
  preamble = "The prior for θ is given by\\n",
  asis = TRUE
)
# S3 method for LogisticIndepBeta
knit_print(
  x,
  ...,
  use_values = TRUE,
  fmt = "%5.2f",
  params = NA,
  tox_label = "DLAE",
  preamble = "The prior for θ is given by\\n",
  asis = TRUE
)
# S3 method for Effloglog
knit_print(
  x,
  ...,
  use_values = TRUE,
  fmt = "%5.2f",
  params = NA,
  tox_label = "DLAE",
  eff_label = "efficacy",
  label = "participant",
  preamble = "The prior for θ is given by\\n",
  asis = TRUE
)
# S3 method for IncrementsRelative
knit_print(x, ..., asis = TRUE)
# S3 method for IncrementsRelativeDLT
knit_print(x, ..., asis = TRUE)
# S3 method for IncrementsDoseLevels
knit_print(x, ..., asis = TRUE)
# S3 method for IncrementsHSRBeta
knit_print(x, ..., asis = TRUE)
# S3 method for IncrementsMin
knit_print(x, ..., asis = TRUE)
# S3 method for IncrementsOrdinal
knit_print(x, ..., asis = TRUE)
# S3 method for IncrementsRelativeParts
knit_print(x, ..., asis = TRUE, tox_label = c("toxicity", "toxicities"))
# S3 method for IncrementsRelativeDLTCurrent
knit_print(x, ..., asis = TRUE, tox_label = c("DLT", "DLTs"))
# S3 method for NextBestMTD
knit_print(
  x,
  ...,
  target_label = "the 25th centile",
  tox_label = "toxicity",
  asis = TRUE
)
# S3 method for NextBestNCRM
knit_print(x, ..., tox_label = "toxicity", asis = TRUE)
# S3 method for NextBestThreePlusThree
knit_print(
  x,
  ...,
  tox_label = c("toxicity", "toxicities"),
  label = "participant",
  asis = TRUE
)
# S3 method for NextBestDualEndpoint
knit_print(
  x,
  ...,
  tox_label = "toxicity",
  biomarker_label = "the biomarker",
  biomarker_units = ifelse(x@target_relative, "%", ""),
  asis = TRUE
)
# S3 method for NextBestMinDist
knit_print(x, ..., tox_label = "toxicity", asis = TRUE)
# S3 method for NextBestInfTheory
knit_print(
  x,
  ...,
  tox_label = "toxicity",
  citation_text = "Mozgunov & Jaki (2019)",
  citation_link = "https://doi.org/10.1002/sim.8450",
  asis = TRUE
)
# S3 method for NextBestTD
knit_print(x, ..., tox_label = "toxicity", asis = TRUE)
# S3 method for NextBestMaxGain
knit_print(x, ..., tox_label = "toxicity", asis = TRUE)
# S3 method for NextBestProbMTDLTE
knit_print(x, ..., tox_label = "toxicity", asis = TRUE)
# S3 method for NextBestProbMTDMinDist
knit_print(x, ..., tox_label = "toxicity", asis = TRUE)
# S3 method for NextBestNCRMLoss
knit_print(
  x,
  ...,
  tox_label = "toxicity",
  asis = TRUE,
  format_func = h_knit_format_func
)
# S3 method for NextBestTDsamples
knit_print(x, ..., tox_label = "toxicity", asis = TRUE)
# S3 method for NextBestMaxGainSamples
knit_print(x, ..., tox_label = "toxicity", asis = TRUE)
# S3 method for NextBestOrdinal
knit_print(x, ..., tox_label = "toxicity", asis = TRUE)
# S3 method for SafetyWindow
knit_print(x, ..., asis = TRUE, time_unit = "day", label = "participant")
# S3 method for SafetyWindowConst
knit_print(
  x,
  ...,
  asis = TRUE,
  label = "participant",
  ordinals = c("first", "second", "third", "fourth", "fifth", "sixth", "seventh",
    "eighth", "ninth", "tenth"),
  time_unit = "day"
)
# S3 method for SafetyWindowSize
knit_print(
  x,
  ...,
  asis = TRUE,
  ordinals = c("first", "second", "third", "fourth", "fifth", "sixth", "seventh",
    "eighth", "ninth", "tenth"),
  label = "participant",
  time_unit = "day",
  level = 2L
)
# S3 method for StoppingOrdinal
knit_print(x, ..., asis = TRUE)
# S3 method for StoppingMaxGainCIRatio
knit_print(x, ..., asis = TRUE)
# S3 method for StoppingList
knit_print(x, ..., preamble, indent = 0L, asis = TRUE)
# S3 method for StoppingAny
knit_print(x, ..., preamble, asis = TRUE)
# S3 method for StoppingAll
knit_print(x, ..., preamble, asis = TRUE)
# S3 method for StoppingTDCIRatio
knit_print(
  x,
  ...,
  dose_label = "the next best dose",
  tox_label = "toxicity",
  fmt_string =
    paste0("%sIf, at %s, the ratio of the upper to the lower limit of the posterior ",
    "95%% credible interval for %s (targetting %2.0f%%) is less than or equal to "),
  asis = TRUE
)
# S3 method for StoppingTargetBiomarker
knit_print(
  x,
  ...,
  dose_label = "the next best dose",
  biomarker_label = "the target biomarker",
  fmt_string =
    paste0("%sIf, at %s, the posterior probability that %s is in the range ",
    "(%.2f, %.2f)%s is %.0f%% or more.\n\n"),
  asis = TRUE
)
# S3 method for StoppingLowestDoseHSRBeta
knit_print(
  x,
  ...,
  tox_label = "toxicity",
  fmt_string =
    paste0("%sIf, using a Hard Stopping Rule with a prior of Beta(%.0f, %.0f), the ",
    "lowest dose in the dose grid has a posterior probability of %s of ",
    "%.0f%% or more.\n\n"),
  asis = TRUE
)
# S3 method for StoppingMTDCV
knit_print(
  x,
  ...,
  fmt_string =
    paste0("%sIf the posterior estimate of the robust coefficient of variation of ",
    "the MTD (targetting %2.0f%%), is than or equal to %.0f%%.\n\n"),
  asis = TRUE
)
# S3 method for StoppingMTDdistribution
knit_print(
  x,
  ...,
  fmt_string =
    "%sIf the mean posterior probability of %s at %.0f%% of %s is at least %4.2f.\n\n",
  dose_label = "the next best dose",
  tox_label = "toxicity",
  asis = TRUE
)
# S3 method for StoppingHighestDose
knit_print(
  x,
  ...,
  dose_label = "the highest dose in the dose grid",
  asis = TRUE
)
# S3 method for StoppingSpecificDose
knit_print(x, ..., dose_label = as.character(x@dose), asis = TRUE)
# S3 method for StoppingTargetProb
knit_print(
  x,
  ...,
  fmt_string =
    paste0("%sIf the probability of %s at %s is in the range [%4.2f, %4.2f] ",
    "is at least %4.2f.\n\n"),
  dose_label = "the next best dose",
  tox_label = "toxicity",
  asis = TRUE
)
# S3 method for StoppingMinCohorts
knit_print(x, ..., asis = TRUE)
# S3 method for StoppingMinPatients
knit_print(x, ..., label = "participant", asis = TRUE)
# S3 method for StoppingPatientsNearDose
knit_print(
  x,
  ...,
  dose_label = "the next best dose",
  label = "participants",
  asis = TRUE
)
# S3 method for StoppingCohortsNearDose
knit_print(x, ..., dose_label = "the next best dose", asis = TRUE)
# S3 method for StoppingMissingDose
knit_print(x, ..., asis = TRUE)

Value

a character string that represents the object in markdown.

The markdown representation of the object, as a character string

a character string that represents the object in markdown.

A character string containing a LaTeX rendition of the object.

a character string that represents the object in markdown.

Arguments

x: (ModelParamsNormal)
the object to be rendered
...: passed to knitr::kable()
asis: (flag)
Not used at present
label: (character)
the term used to label participants
tox_label: (character)
the term used to describe toxicity
level: (count)
the markdown level at which the headings for cohort size will be printed. An integer between 1 and 6
title: (character) The text of the heading of the section describing the design
sections: (character) a named vector of length at least 4 defining the headings used to define the sections corresponding to the design's slots. The element names must match the Design's slot names.
full_grid: (flag)
Should the full dose grid appear in the output table or simply those doses for whom at least one evaluable participant is available? Ignored unless summarise == "dose".
summarise: (character)
How to summarise the observed data. The default, "none", lists observed data at the participant level. "dose" presents participant counts by dose and "cohort" by cohort.
summarize: (character)
Synonym for summarise
units: (character)
The units in which the values in doseGrid are
format_func: (function)
The function used to format the range table.
use_values: (flag)
print the values associated with hyperparameters, or the symbols used to define the hyper-parameters. That is, for example, mu or 1.
fmt: (character)
the sprintf format string used to render numerical values. Ignored if use_values is FALSE.
biomarker_label: (character)
the term used to describe the biomarker
params: (character)
The names of the model parameters. See Usage Notes below.
preamble: (character)
the text that introduces the list of rules
theta: (character)
the LaTeX representation of the theta vector
eff_label: (character)
the term used to describe efficacy
target_label: (character)
the term used to describe the target toxicity rate
biomarker_units: (character)
the units in which the biomarker is measured
citation_text: (character)
the text used to cite Mozgunov & Jaki
citation_link: (character)
the link to Mozgunov & Jaki
time_unit: (character)
the word used to describe units of time. See Usage Notes below.
ordinals: (character)
a character vector whose nth defines the word used as the written representation of the nth ordinal number.
indent: (integer)
the indent level of the current stopping rule list. Spaces with length indent * 4 will be prepended to the beginning of the rendered stopping rule list.
dose_label: (character)
the term used to describe the target dose
fmt_string: (character)
the character string that defines the format of the output

Usage Notes

label describes the trial's participants.

It should be a character vector of length 1 or 2. If of length 2, the first element describes a cohort_size of 1 and the second describes all other cohort_sizes. If of length 1, the character s is appended to the value when cohort_size is not 1.

The default value of col.names is c("Lower", "Upper", "Cohort size") and that of caption is "Defined by the dose to be used in the next cohort". These values can be overridden by passing col.names and caption in the function call.

The by default, the columns are labelled Lower, Upper and Cohort size. The table's caption is Defined by the number of <tox_label[2]> so far observed. These values can be overridden by passing col.names and caption in the function call.

label describes the trial's participants.

It should be a character vector of length 1 or 2. If of length 2, the first element describes a single participant and the second describes all other situations. If of length 1, the character s is appended to the value when the number of participants is not 1. The default values of col.names and caption vary depending on the summary requested. The default values can be overridden by passing col.names and caption in the function call.

params must be a character vector of length equal to that of x@mean (and x@cov). Its values represent the parameters of the model as entries in the vector theta, on the left-hand side of "~" in the definition of the prior. If named, names should be valid LaTeX, escaped as usual for R character variables. For example, "\\alpha" or "\\beta_0". If unnamed, names are constructed by pre-pending an escaped backslash to each value provided.

The default value of col.names is c("Min", "Max", "Increment") and that of caption is "Defined by highest dose administered so far". These values can be overridden by passing col.names and caption in the function call.

The default value of col.names is c("Min", "Max", "Increment") and that of caption is "Defined by number of DLTs reported so far". These values can be overridden by passing col.names and caption in the function call.

label defines how toxicities are described.

It should be a character vector of length 1 or 2. If of length 2, the first element describes a single toxicity and the second describes all other toxicity counts. If of length 1, the character s is appended to the value describing a single toxicity.

The default value of col.names is c("Min", "Max", "Increment") and that of caption is "Defined by number of DLTs in the current cohort". These values can be overridden by passing col.names and caption in the function call.

tox_label defines how toxicities are described.

This section describes the use of label and tox_label, collectively referred to as labels. A label should be a scalar or a vector of length 2. If a scalar, it is converted by adding a second element that is equal to the first, suffixed by s. For example, tox_label = "DLT" becomes tox_label = c("DLT", "DLTs"). The first element of the vector is used to describe a count of 1. The second is used in all other cases.

To use a BibTeX-style citation, specify (for example) citation_text = "@MOZGUNOV", citation_link = "".

label should be a character vector of length 1 or 2. If of length 2, the first element describes a count of 1 and the second describes all other counts. If of length 1, the character s is appended to the value when the count is not 1.

label and time_unit are, collectively, labels.

A label should be a character vector of length 1 or 2. If of length 2, the first element describes a count of 1 and the second describes all other counts. If of length 1, the character s is appended to the value when the count is not 1.

label describes the trial's participants.

label defines how toxicities are described.

tox_label defines how toxicities are described.

To use a BibTeX-style citation, specify (for example) citation_text = "@MOZGUNOV", citation_link = "".

label and time_unit are, collectively, labels.

Description

Usage

Value

Arguments

Usage Notes

See Also