High-Performance Open-Source Archive
Surv() and strata() from the
survival package so that user-supplied formulas in action
functions (e.g. Surv(os, os_event) ~ arm) work after
library(TrialSimulator) alone, without requiring
library(survival) or the survival:: prefix.
This fixes errors in parallel runs (n_workers > 1) where
each worker only attaches TrialSimulator (reported in
#14).'or' would error out when not all of its branches
could be reached, instead of resolving to the reachable branch.eventNumber() and enrollment() conditions.
Wall-time reduction ranges from a few percent to ~20% depending on how
condition-evaluation-heavy the design is, with no change to results. Set
options(trialsimulator.use_cpp = FALSE) to fall back to the
original R implementation.doseRanging.Rmd now loads pre-computed simulation output;
simulatePfsAndOsGumbel.Rmd uses a smaller validation
sample).CorrelatedPfsAndOs2() to simulate correlated PFS
and OS using a Gumbel copula while targeting marginal PFS/OS medians and
Kendall’s tau between observed, uncensored PFS and OS times.dplyr::filter() call in
Trials$get_event_tables() when no filter expression is
provided, reducing total simulation wall time by roughly 5-10% in trials
with frequent milestone condition checks.n_switches column to locked data returned by
get_locked_data(), counting the number of treatment
switches per patient within the data lock window.expandRegimen() to expand the
regimen_trajectory column in locked data into a long-format
data frame with one row per regimen segment per patient.trial() using argument
stratification_factors.... in regimen() to pass arguments
to what(), when() and how().regimen() to support both single or
multiple switching.regimen().enrollment() to define milestone. This bug does not affect
milestone triggering.tidy (default: TRUE) in
Controllers$run() to stop computing and saving event count
per arm per endpoint for 40% shorter run time.solvePiecewiseConstantExponentialDistribution() to compute
event rates in time windows given survival probabilities at
changepoints.qPiecewiseExponent(), the quantile function of
piecewise exponential distribution. This function is useful to simulate
time-to-event endpoint that is correlated to other endpoints using the
copula method. For example, the simdata package needs
marginal quantile functions.Controllers$run() through
new argument n_workers. The package mirai is
used. Although mirai advocates the use of L’Ecuyer-CMRG
streams to maintain independence between multiple streams, however,
TrialSimulator resets it to be Mersenne-Twister streams to
force identical behavior between n_workers = 1 and
n_workers > 1. This enables debuggability and
reproduciability under single-process mode by setting seed that causes
issues under multi-process mode.remove_arms(), add_arms(),
update_sample_ratio(), set_duration(),
resize() and update_generator().Trials$resize() that resizes an
ongoing trial.get_output() by setting new argument
tidy = TRUE.Trials$update_generator().enforce = TRUE is no longer needed when adding new arms
to an existing trial with at least one arm through
Trials$add_arms(). However, for backward compatibility,
legacy codes with enforce = TRUE still behaves as expected
and no need to update.milestone_name. Now action function only requires argument
trial and supports optional arguments.milestone() now support ... to pass
arguments to action functions.enrollment() now supports
min_treatment_duration to ensure minimum treatment duration
received by patients at a milestone. With its default value 0, milestone
is triggered when a specific number of patients are enrolled.invisible(NULL) is no longer recommended as return value of
action function.event_plot() for plot of
cumulative events number when endpoint name is "ep". This
is due to data masking in dplyr.update_sample_ratio. This test is probably broken by
randomness (it is okay).summarizeMilestoneTime and its plot method
to summarize triggering time of milestones.update_sample_ratio() now supports updating multiple
arms simultaneously. When ratio is not a whole number,
sample() is used to replace the permuted block algorithm to
randomize patients to arms. This enable response-adaptive design.update_sample_ratio().-Add vignette of fixed design. -Add vignette of wrapper functions of commom statistical tests.
enroll_patients. This is useful when a milestone is
triggered after all patients are recruited.... in eventNumber to count event
in subset of trial data. This is useful in enrichment design when
milestone is defined based on biomarker.get as alias of
get_custom_data in the Trials class.trial$dunnettTest(..., planned_info = "default") triggers
an error.Trials$dunnettTest to be compatible to one-sided
logrank test.fitLogrank now supports
formula. strata(...) can be in
formula. No covariate is accepted.fitLogistic.fitLogistic now supports
scale = "coefficient" to compute regression coefficient as
main effect of arm in the presence of covariates. It is
also equivalent to scale = "log odds ratio" in the absence
of covariates.fitLogistic.fitCoxph now supports formula
to compute log hazard ratio or hazard ratio as main effect of
arm. It will detect whether arm’s main effect is specified
in formula. It allows covariates and interaction between covariates and
arm. However, only the main effect of arm is tested and returned.fitCoxph.fitLogistic now supports computing log
odds ratio, odds ratio, risk ratio, and risk difference using
emmeans contrast, in the presence of covariates.fitLogistic and
fitLinear.fitLogistic now supports
formula with covariates, and uses emmeans
contrast to compute average treatment effect (ATE) on the
logit scale.fitLinear now supports
formula with covariates, and uses emmeans
contrast to compute average treatment effect (ATE) on the mean
scale.trigger_condition in the function
milestone is deprecated and is replaced with
when. Note that trigger_condition is still
supported in the R6 class Milestones.trial$get_custom_data throws an error
when n > 1 in controller$run(n) because
custom data is wiped out in trial$reset.arm through the
... argument.rmarkdown.rmarkdown.controller() as per
suggestion from CRAN team.message() so
that Shiny app can display it properly.solveMixtureExponentialDistribution to
compute median of exponential endpoint of subgroup or the overall
population.endpoints, arm,
trial, event, listener and
controller for Endpoint$new,
Arm$new, Trial$new, Event$new,
Listener$new and Controller$new.Trial$set_duration.enroll_a_patient. Use
enroll_patients only.n > 1 in
Controller$run when an arm can possibly be removed
adaptively during a trial.enrollment. This is useful to
count randomized patients of all arms even if some are removed
adaptively.GraphicalTesting
and graphicalMCP to repository TrialSimulatorDocuments.Controller$run now can specify number of simulation
replicates by newly added argument n. If n is
greater than 1, simulation results can be accessed in
Controller$get_output().StaggeredRecruiter to force the enrollment
time of the first patient is zero. This is an known issue but I was too
lazy to fix it. Earlier version may have overestimated time of
events.fitFarringtonManning of Farrington-Manning
test for rate difference.Trial$bind to row bind data frame in
action functions. It is useful to prepare inputs of group sequential or
graphical test.weibullDropout to compute parameters of
Weibull distribution when using it for dropout distribution.1e-5 while no alpha should have been propagated.enrollment, eventNumber and
calendarTime can be combined with & and
|. Nested combination is supported by using
parentheses.TriggerByEventNumbers and
TriggerByCalendarTime are therefore deprecated.fitLogistic to fit logistic regression
model.generator in
Endpoint.GraphicalTesting when a hypothesis is
tested multiple times at the same stage because more alpha is passed
from other rejected hypothesis.do_nothing() if users
have no intent to do anything at a triggered event. This function can be
passed to the argument action when creating a new event,
e.g.,
Event$new(name = 'interim', trigger_condition = TriggerByCalendarTime, action = do_nothing, calendar_time = 64).Controller$run() and insert error
message into output (see Trial$get_output()$error_message).
It helps to integrate TrialSimulator with
targets.Trial$get_seed().GraphicalTesting based on simplified interface
of GroupSequentialTest.
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