Render a CohortSizeConst Object
Source: R/helpers_knitr_CohortSize.R, R/helpers_knitr_Design.R, R/helpers_knitr_GeneralData.R, and 5 more
knit_print.Rd![[Experimental]](figures/lifecycle-experimental.svg)
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 class 'CohortSizeConst'
knit_print(x, ..., asis = TRUE, label = c("participant", "participants"))
# S3 method for class 'CohortSizeRange'
knit_print(x, ..., asis = TRUE)
# S3 method for class 'CohortSizeDLT'
knit_print(x, ..., tox_label = "toxicity", asis = TRUE)
# S3 method for class 'CohortSizeParts'
knit_print(x, ..., asis = TRUE, label = c("participant", "participants"))
# S3 method for class 'CohortSizeMax'
knit_print(x, ..., asis = TRUE)
# S3 method for class 'CohortSizeMin'
knit_print(x, ..., asis = TRUE)
# S3 method for class 'CohortSizeOrdinal'
knit_print(x, ..., tox_label = "toxicity", asis = TRUE)
# S3 method for class 'StartingDose'
knit_print(x, ..., asis = TRUE)
# S3 method for class 'RuleDesign'
knit_print(x, ..., level = 2L, title = "Design", sections = NA, asis = TRUE)
# S3 method for class 'Design'
knit_print(x, ..., level = 2L, title = "Design", sections = NA, asis = TRUE)
# S3 method for class 'DualDesign'
knit_print(x, ..., level = 2L, title = "Design", sections = NA, asis = TRUE)
# S3 method for class 'DADesign'
knit_print(x, ..., level = 2L, title = "Design", sections = NA, asis = TRUE)
# S3 method for class 'TDDesign'
knit_print(x, ..., level = 2L, title = "Design", sections = NA, asis = TRUE)
# S3 method for class 'DualResponsesDesign'
knit_print(x, ..., level = 2L, title = "Design", sections = NA, asis = TRUE)
# S3 method for class 'DesignOrdinal'
knit_print(x, ..., level = 2L, title = "Design", sections = NA, asis = TRUE)
# S3 method for class '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 class 'TDsamplesDesign'
knit_print(x, ..., level = 2L, title = "Design", sections = NA, asis = TRUE)
# S3 method for class 'DualResponsesDesign'
knit_print(x, ..., level = 2L, title = "Design", sections = NA, asis = TRUE)
# S3 method for class 'DualResponsesSamplesDesign'
knit_print(x, ..., level = 2L, title = "Design", sections = NA, asis = TRUE)
# S3 method for class 'RuleDesignOrdinal'
knit_print(x, ..., level = 2L, title = "Design", sections = NA, asis = TRUE)
# S3 method for class 'GeneralData'
knit_print(
x,
...,
asis = TRUE,
label = c("participant", "participants"),
full_grid = FALSE,
summarise = c("none", "dose", "cohort"),
summarize = summarise,
units = NA,
format_func = function(x) x
)
# S3 method for class 'DataParts'
knit_print(
x,
...,
asis = TRUE,
label = c("participant", "participants"),
full_grid = FALSE,
summarise = c("none", "dose", "cohort"),
summarize = summarise,
units = NA,
format_func = function(x) x
)
# S3 method for class 'DualEndpoint'
knit_print(
x,
...,
asis = TRUE,
use_values = TRUE,
fmt = "%5.2f",
units = NA,
tox_label = "toxicity",
biomarker_label = "PD biomarker"
)
# S3 method for class '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 class 'GeneralModel'
knit_print(
x,
...,
params = c("alpha", "beta"),
asis = TRUE,
use_values = TRUE,
fmt = "%5.2f",
units = NA
)
# S3 method for class 'LogisticKadane'
knit_print(
x,
...,
asis = TRUE,
use_values = TRUE,
fmt = "%5.2f",
units = NA,
tox_label = "toxicity"
)
# S3 method for class 'LogisticKadaneBetaGamma'
knit_print(
x,
...,
asis = TRUE,
use_values = TRUE,
fmt = "%5.2f",
tox_label = "toxicity",
units = NA
)
# S3 method for class '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 class 'LogisticLogNormalMixture'
knit_print(x, ..., asis = TRUE, use_values = TRUE, fmt = "%5.2f", units = NA)
# S3 method for class '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 class 'LogisticNormalMixture'
knit_print(x, ..., asis = TRUE, use_values = TRUE, fmt = "%5.2f", units = NA)
# S3 method for class 'LogisticNormalFixedMixture'
knit_print(x, ..., asis = TRUE, use_values = TRUE, fmt = "%5.2f", units = NA)
# S3 method for class 'OneParLogNormalPrior'
knit_print(
x,
...,
tox_label = "toxicity",
asis = TRUE,
use_values = TRUE,
fmt = "%5.2f"
)
# S3 method for class 'OneParExpPrior'
knit_print(x, ..., asis = TRUE)
# S3 method for class '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 class '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 class '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 class '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 class 'IncrementsRelative'
knit_print(x, ..., asis = TRUE)
# S3 method for class 'IncrementsRelativeDLT'
knit_print(x, ..., asis = TRUE)
# S3 method for class 'IncrementsDoseLevels'
knit_print(x, ..., asis = TRUE)
# S3 method for class 'IncrementsHSRBeta'
knit_print(x, ..., asis = TRUE)
# S3 method for class 'IncrementsMin'
knit_print(x, ..., asis = TRUE)
# S3 method for class 'IncrementsOrdinal'
knit_print(x, ..., asis = TRUE)
# S3 method for class 'IncrementsRelativeParts'
knit_print(x, ..., asis = TRUE, tox_label = c("toxicity", "toxicities"))
# S3 method for class 'IncrementsRelativeDLTCurrent'
knit_print(x, ..., asis = TRUE, tox_label = c("DLT", "DLTs"))
# S3 method for class 'NextBestMTD'
knit_print(
x,
...,
target_label = "the 25th centile",
tox_label = "toxicity",
asis = TRUE
)
# S3 method for class 'NextBestNCRM'
knit_print(x, ..., tox_label = "toxicity", asis = TRUE)
# S3 method for class 'NextBestThreePlusThree'
knit_print(
x,
...,
tox_label = c("toxicity", "toxicities"),
label = "participant",
asis = TRUE
)
# S3 method for class 'NextBestDualEndpoint'
knit_print(
x,
...,
tox_label = "toxicity",
biomarker_label = "the biomarker",
biomarker_units = ifelse(x@target_relative, "%", ""),
asis = TRUE
)
# S3 method for class 'NextBestMinDist'
knit_print(x, ..., tox_label = "toxicity", asis = TRUE)
# S3 method for class '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 class 'NextBestTD'
knit_print(x, ..., tox_label = "toxicity", asis = TRUE)
# S3 method for class 'NextBestMaxGain'
knit_print(x, ..., tox_label = "toxicity", asis = TRUE)
# S3 method for class 'NextBestProbMTDLTE'
knit_print(x, ..., tox_label = "toxicity", asis = TRUE)
# S3 method for class 'NextBestProbMTDMinDist'
knit_print(x, ..., tox_label = "toxicity", asis = TRUE)
# S3 method for class 'NextBestNCRMLoss'
knit_print(
x,
...,
tox_label = "toxicity",
asis = TRUE,
format_func = function(x) {
kableExtra::kable_styling(x, bootstrap_options =
c("striped", "hover", "condensed"))
}
)
# S3 method for class 'NextBestTDsamples'
knit_print(x, ..., tox_label = "toxicity", asis = TRUE)
# S3 method for class 'NextBestMaxGainSamples'
knit_print(x, ..., tox_label = "toxicity", asis = TRUE)
# S3 method for class 'NextBestOrdinal'
knit_print(x, ..., tox_label = "toxicity", asis = TRUE)
# S3 method for class 'SafetyWindow'
knit_print(x, ..., asis = TRUE, time_unit = "day", label = "participant")
# S3 method for class '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 class '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 class 'StoppingOrdinal'
knit_print(x, ..., asis = TRUE)
# S3 method for class 'StoppingMaxGainCIRatio'
knit_print(x, ..., asis = TRUE)
# S3 method for class 'StoppingList'
knit_print(x, ..., preamble, indent = 0L, asis = TRUE)
# S3 method for class 'StoppingAny'
knit_print(x, ..., preamble, asis = TRUE)
# S3 method for class 'StoppingAll'
knit_print(x, ..., preamble, asis = TRUE)
# S3 method for class '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 class '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 class '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 class '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 class '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 class 'StoppingHighestDose'
knit_print(
x,
...,
dose_label = "the highest dose in the dose grid",
asis = TRUE
)
# S3 method for class 'StoppingSpecificDose'
knit_print(x, ..., dose_label = as.character(x@dose), asis = TRUE)
# S3 method for class '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 class 'StoppingMinCohorts'
knit_print(x, ..., asis = TRUE)
# S3 method for class 'StoppingMinPatients'
knit_print(x, ..., label = "participant", asis = TRUE)
# S3 method for class 'StoppingPatientsNearDose'
knit_print(
x,
...,
dose_label = "the next best dose",
label = "participants",
asis = TRUE
)
# S3 method for class 'StoppingCohortsNearDose'
knit_print(x, ..., dose_label = "the next best dose", asis = TRUE)
# S3 method for class 'StoppingMissingDose'
knit_print(x, ..., asis = TRUE)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 unlesssummarise == "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 forsummarise- units
(
character)
The units in which the values indoseGridare- 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)
thesprintfformat string used to render numerical values. Ignored ifuse_valuesisFALSE.- 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 lengthindent * 4will 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
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.
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.
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.
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.
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.
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.
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.