This release marks yet another major update of the package: Backfill
cohort simulations are now supported in the Design class,
powered by the new Backfill class. This also leads to
breaking changes in the Data and
GeneralSimulationsSummary classes, which gains new slots,
too. Please regenerate any existing Dataor
Designas well as resulting SimulationsSummary
objects; serialized objects from previous versions will not be
compatible.
New Features
A new vignette trial_backfill has been added which
describes how to use backfill cohorts in crmPack.
Added new Backfill class which allows to specify
backfill cohorts as a new slot in the Design objects.
Added new slots backfilled and response in
the Data class to capture the information whether each
patient has been backfilled, and whether a (binary) efficacy response
was observed (in order to support backfilling rules which depend on a
minimum number of efficacy responses).
The plot method for Data objects allows to
optionally exclude backfill patients, and mark backfill and response
information.
Added new virtual classes Opening and
Recruitment and corresponding instantiable subclasses to
allow for flexible specification of backfill cohort opening and
recruitment rules.
Added new CohortSizeRandom class to allow for random
cohort sizes.
Simulations of Design objects now support backfill
cohorts.
GeneralSimulationsSummary got new slots
any_backfilled, n_backfilland
backfill_doses informing whether any backfilling was
planned or took place, the number of backfill patients and the doses of
the backfill patients across the simulations. The corresponding
show method was updated to display this information to the
user.
GeneralSimulations got a simple show
method to avoid printing the whole structure to the user, which is
probably not helpful.
StoppingPatientsNearDose got a new slot
include_backfill to allow for optionally excluding
backfilled patients from the patient count.
Version 2.0.2
Miscellaneous
Fixed logging tests which had been broken by recent
futile.logger updates.
Version 2.0.1
Bugfixes
The validation of LogisticNormalFixedMixture objects
did not allow for a floating point calculation deviation in the sum of
the weights when comparing them to 1, which was caught by the
M1mac check on CRAN. This has been fixed now.
Version 2.0.0
This release signifies a major breaking revamp of the package. Over
several years, the package has undergone significant cleanup, tests
writing and refactoring efforts. The package has thereby reached a major
milestone and currently includes:
15 different dose-toxicity models
15 different dose recommendation rules
19 different stopping rules
9 cohort size rules
9 increment rules
Reporting functionality (using knitr)
13 vignettes (documentation)
Users are advised to carefully review the release notes and
documentation for detailed information on the changes and any necessary
updates to their existing code using a previous CRAN release of
crmPack. In particular, there is a new vignette which
describes how to use certain functions and features of
crmPack after the major refactoring.
New Features
Added new NextBestNCRMLoss and
NextBestEWOC classes and corresponding
nextBest methods.
Added new DataGrouped and DesignGrouped
classes with corresponding model LogisticLogNormalGrouped
to support simultaneous dose escalation with monotherapy and combination
therapy arms.
Provided basic support for ordinal CRM models in
DesignOrdinal (simulation is not yet supported). See the
vignette for more details.
Included rolling CRM design implemented in DADesign.
See the vignette for more details.
Created new ProbitLogNormalRel model class to support
the (standardized) dose.
Implemented knit_print methods for almost all
crmPack classes to improve rendering in Markdown and Quarto
documents. See the vignette for more details.
Implemented broom-like tidy methods for
all concrete crmPack classes. See the vignette for more
details.
Added logging via the futile.logger package, the user
interface consists of four functions: enable_logging,
disable_logging, is_logging_enabled,
log_trace.
Created the CrmPackClass class as the ultimate ancestor
of all other crmPack classes to allow identification of
crmPack classes and simpler definition of generic methods.
Added no-parameter constructor functions named
.Default<class name> to provide usable instances of
all concrete subclasses of Increments, Model,
NextBest and Stopping.
Included additional_stats to add reporting of
additional parameters to method simulate to summarize
MTD.
report_label can be added to stopping rules for
individual or combined stopping rule reporting.
Implemented the IncrementsMaxToxProb class.
Enhancements
approximate now returns a list containing
the fitted model and, optionally, a ggplot object of the
approximated dose/toxicity curve.
Modified efficacy-EffFlexi method: allowed for
vectorized dose; NA is now returned for doses from outside
of the dose grid range (and the warning is thrown).
doselimit argument in nextBest method is
now specified as Inf instead of
numeric(0).
Warning message not printed anymore by nextBest methods
when doselimit not specified.
Allowed for from_prior flag - argument to
modelspecs function at GeneralModel
class.
Introduced validation of the updated object for update
methods for Data-like classes. Added check
flag to possibly omit the validation of the updated object.
Added new functions dose_grid_range and
ngrid, which return the range and the number of doses in
the dose grid, respectively.
Added methods names and size for objects
of class Samples.
Bugfixes
Corrected the spelling of the name of the messgae [sic]
attribute of the return value of stopTrial with signature
stopping = "StoppingTDCIRatio".
Refactoring
Set up the package to use testthat and added unit and
integration tests.
Started using the lifecycle package to manage
deprecations and breaking changes.
Changed ProbitLogNormal so that it supports the log of
(standardized) dose only.
Replaced warning with message when no cohort or
ID is provided to the user constructor
Data.
Re-factored sampleSize function so that it returns
0 if burnin > iterations.
Miscellaneous
Removed multiplot function. Please use equivalent
functionality in other packages, such as cowplot or
ggpubr.
Version 1.0.0
Reference JSS publication.
Version 0.2.9
By default only use 5 cores and not all available cores on a
machine. Note that this value can also be changed by the user.
Change of maintainer
Version 0.2.8
Bugfixes:
PLcohortSize now defaults to 0 placebo patients upon Design class
initialization (instead of 1 before - but note that this did not have
effect on erroneous simulations, due to option being set in Data
class)
The “examine” function also stops when the stopping rules are
fulfilled already in case of no DLTs occurring. This was not the case
beforehand and could lead to infinite looping (thanks to John
Kirkpatrick for reporting the bug)
Removed RW2 warnings in “DualEndpointRW” - it seems to work nicely
now (thanks to Charles Warne for reporting!)
Removed WinBUGS since it was not used anyway (and paper does not
describe it)
New features:
The “examine” function now counts the number of times the same dose
is recommended contiguously and break after e.g. the default 100 times
(can be specified in a new option of “examine”) to further avoid
infinite loops and issues a corresponding warning if this condition is
met
New “Increments” class “IncrementsNumDoseLevels” that works directly
on the number of dose levels in the dose grid that can be incremented to
from the current to the next cohort (thanks to John Kirkpatrick for the
suggestion). This can for example be used in order to force the design
not to skip any dose level when escalating.
Included the JSS manuscript as a new vignette.
It is now possible to specify how many cores should be used when
parallel computations are used.
Version 0.2.7
Bugfixes:
LogisticNormal now works again - prec was
not found before.
Version 0.2.6
Bugfixes:
Replaced BayesLogit dependency by JAGS code, since
BayesLogit was taken off CRAN.
Speed up one example to pass CRAN check.
Version 0.2.5
New features:
matching of doses with the dose grid now includes a tolerance of
1e-10, in order to make it more user-friendly (thanks to YJ Choi and
Giuseppe for investigating)
Bugfixes:
documentation:
minor fix for alpha1 description in LogisticLogNormal-class
minor fix on scale_colour_manual import from ggplot2 reported by
R-Core
Version 0.2.4
New features:
In case of multiple nextBest plots these are now also returned as
original plots in the list singlePlots, to allow for further
customization, before jointly plotting them.
ProbitLogNormal: Now also this model allows for a reference dose and
a log transformation of the (standardized) dose. This can be specified
with options refDose and useLogDose.
DualEndpoint: Same additional options as for ProbitLogNormal are now
available for the DualEndpoint models. As a consequence, the parameter
“refDose” for class DualEndpointBeta needed to be renamed to
“refDoseBeta”, and the parameter “refDose” for class DualEndpointEmax
renamed to “refDoseEmax”.
Bugfixes:
documentation: in the DualEndpoint description fixed the problem in
the formula
Version 0.2.3
New features:
New increment class “IncrementMin” has been added which allows to
combine multiple increment rules with the MIN operation
Version 0.2.1
New features:
Option targetThresh for NextBestDualEndpoint allows to tune from
which target probability onwards it will be used to derive the next best
dose (before this was fixed to 0.05)
Added ProbitLogNormal model
In the NextBestDualEndpoint class, the additional option “scale” now
allows to also specify absolute biomarker target ranges. In the
corresponding method evaluation, the safety samples are now no longer
included in the evaluation of the biomarker target probability, such
that now the description is consistent with the computations.
NextBestNCRM and NextBestDualEndpoint now return the matrix of
target and overdosing probabilities as additional list element “probs”
in the result of “nextBest” applied.
Note that in the StoppingTargetBiomarker evaluation, the toxicity is
no longer a part of the biomarker target probability.
Bugfixes:
Added back the example vignette, so that it can be opened with
crmPackExample()
Clarified that for the DualEndpointRW model samples from the prior
cannot be obtained due to impropriety of the RW prior (added to model
class description).
For DualEndpointRW models, it is now possible to have
non-equidistant grid points, and obtain sensible results. (But still
needs to be thoroughly tested though.)
For DualEndpointBeta model, it is now possible to have negative E0
and Emax parameters.
Cohort size of 0 for placebo is now possible - e.g. to only start
with patients and then later move to larger cohorts also including
placebo subjects.
When simulating with firstSeparate=TRUE and placebo, now the first
(sentinel) cohort includes one active and one placebo patients, and the
next patients use the cohort size for the active and placebo arms,
respectively.
Barplots work now also when there was only one observed value in all
simulations
NextBestDualEndpoint now only takes into account active doses when
optimizing the biomarker outcome for the next best dose among admissible
doses, thus avoiding early stopping at the placebo dose level.
If DataMixture objects are used, mcmc now correctly sets fromPrior
to FALSE if the shared data object contains any data.
Version 0.2.0
Added arguments probmin and probmax to MinimalInformative in
order to control the probability threshold at the minimum and maximum
dose for the minimally informative prior
Values of 95% CI and the corresponding ratio of the upper to the
lower limit of this CI are displayed in results when using
‘nextBest’
The six- number summary tables including the values of the
lowest, 25th percentile, 50th percentile or the median, the mean, the
75th precentile and the highest of the final (at stopping) estimates of
the
dose levels corresponds to the target probability of DLE used at the
end of a trial, TDEOT
ratios of the upper to the lower 95% credibility intervals (CI) of
TDEOT
dose levels corresponds to the target probability of DLE used during
a trial
dose levels corresponds to the maximum gain value, Gstar
ratios of the upper to the lower 95% CI of the final estimates of
Gstar
optimal doses, either the TDEOT (for DLE response only) or the
minimum of TDEOT and Gstar (for DLE and efficacy response)
ratios of the optimal dose
across all simulations will also be displayed when using ‘summary’
for simulations.
Version 0.1.8
The value of the 95% CI of the final estimates will be displayed
in results when using ‘stopTrial’
Bugfixes for dual endpoint designs:
Improved graphical display in plots for nextBest dose
Improved methodology to compute Gstar
Warnings are removed when using nextBest in simulations
Stopping rules can now also be freely combined using the and/or
operators with the dual endpoint design stopping rules not using MCMC
samples.
Version 0.1.6
New model class “LogisticLogNormalMixture” has been added, for use
with the new data class “DataMixture”.
New stopping rule “StoppingHighestDose” has been added.
The “examine” method no longer stops when two consecutive cohorts
start with the same dose. This is important e.g. for the two-parts study
designs, where part 1 can end with the same dose as part 2 starts.
The contents of the “datanames” slot of new models are no longer
restricted to a specific set, which was previously enforced by the
validation function of the GeneralModel and AllModels classes.
Sampling from the prior can now be enabled/disabled by the user for
the mcmc function, which is necessary for models where it might not be
from the prior even though nObs == 0.
Bugfix: The results from the MinimalInformative function were not
reproducible beforehand. Now a seed parameter can be supplied, which
ensures reproducibility.
Bugfix: Compatibility of help file links with new ggplot2 package
version.
Version 0.1.5
Bugfix: In newer versions of grid the plotting of simulation objects
did no longer work. This was fixed.
Version 0.1.2
Bugfix: The MinimalInformative function previously produced too
uninformative prior quantiles, which were not fulfilling the
requirements in the function’s documentation. With this bugfix, the
correct (as per the Neuenschwander et al
publication) prior quantiles are specified and then approximated
with logistic (log) normal priors.
Version 0.1.1
Bugfix: Previously, it could happen with NextBestNCRM rule, that
higher doses lead to decreasing probability of overdosing, only because
for some doses there was numerically probability 1 of having a DLT. With
this bugfix, it was clarified in the rules documentation and fixed in
the rule method, that the right limit of the overdose interval vector
will be inclusive.
Version 0.1.0
Added examine function to generate a table of hypothetical trial
courses for model-based and rule-based DLT-endpoint designs
Made results from mcmc() (works with the usual set.seed in earlier
user code) and simulate() (as previously already promised) reproducible.
See help file for mcmc for more details. Additional improvements to
reduce confusing warning messages / notes from mcmc() and higher-level
functions.
Made simulate with parallel=TRUE work on r.roche.com (Linux server),
using the same parallelization method as for laptops (Windows)
Passing an empty (zero length) vector as the doselimit parameter of
the nextBest function is now considered as requesting a dose
recommendation without a strict dose limit, and a corresponding warning
is printed.
Introduced GeneralModel class, from which then the class Model for
single agent dose escalation derives. Another branch will be the
ComboLogistic model for multiple agent combinations (in a future
version). Similarly introduced GeneralData class, from which the class
Data for single agent derives, separately from that will be the subclass
DataCombo (in a future version).
Version 0.0.23
Fixed bug in mcmc function which led to error “all data elements
must have as many rows as the sample size was” and slightly changed JAGS
way of handling burnin / thinning (which should not have a user
impact).
Reduced number of MCMC samples for dual-endpoint example in vignette
to be able to plot the vignette
Version 0.0.22
simulate function has been fixed (specification of arguments)
Dual-endpoint model-based design has been added.
3+3 design simulation is now possible, see
?ThreePlusThreeDesign
Welcome message on attaching crmPack, i.e. when library(“crmPack”)
is run
crmPackUpgrade() function for easy upgrade of crmPack to the latest
version
Rule-based designs now can be specified with the class RuleDesign,
while the model-based designs stay with the class Design. An even more
special class is the DualDesign class, for dual-endpoint model-based
designs. Corresponding classes GeneralSimulations, Simulations and
DualSimulations capture the output of the trial simulations for
rule-based, model-based and dual-endpoint designs.
The class Simulations-summary has been renamed to
SimulationsSummary, similarly for the classes GeneralSimulationsSummary
and DualSimulationsSummary.
All Stopping and CohortSize rules that are based on intervals
(IncrementsRelative, IncrementsRelativeDLT, CohortSizeRange,
CohortSizeDLT) now use a different intervals definition. Now the
“intervals” slots only contain the left bounds of the intervals. Before,
the last element needed to be infinity. See the vignette for
examples.
StoppingMaxPatients class has been removed, as it was redundant with
the class StoppingMinPatients. Please just use the StoppingMinPatients
class instead.
Initialization methods have been replaced by dedicated
initialization functions. Please now use these Class(…) functions
instead of new(“Class”, …) calls to obtain the correct objects. This
change is also reflected in the vignette.
The extract function for extracting parameter samples from Samples
objects has been removed (due to a name conflict with ggmcmc dependency
packages). Please now use instead the “get” method for Samples objects
(see the vignette for an example) to obtain data in the ggmcmc
format.
crmPack now needs the package httr (it’s now in the “Imports”
field). Packages Rcpp and RcppArmadillo have been moved from “Depends”
to “Suggests” packages. Currently we are not using them at all.
showLegend argument for model fit plotting functions, in order to
show the legend or not.