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Version 1.0.0 marks the first stable major release. Highlights include observed interim evaluation, RMST and Farrington-Manning analyses, immediate-success stopping, arm-specific priors, and Monte Carlo and calendar-time summaries.
prop_loss now means the probability of dropout by
end_of_study under an independent exponential distribution,
rather than a fixed fraction removed from the trial. Values must be in
[0, 1); prop_loss = 1 is invalid.empty_interval = "propagate" explicitly only when the
legacy adjacent- interval heuristic is intended.prior_surv generates
outstanding outcomes and prior_surv_final analyzes each
completed predictive trial as well as the actual final trial. Supply
both explicitly to separate the roles.method = "riskdiff" with
"riskdiff-wald" to retain the Wald analysis, or choose
"riskdiff-fm" for the Farrington-Manning score test. The
old name remains a deprecated alias. Genuine FM final imputation is
unsupported, and zero total variance in Rubin pooling is
non-estimable.sim_comp_data(), rename
cutpoints to generation_cutpoints. The
simulation entry points retain cutpoints for the analysis
partition and accept generation_cutpoints separately.N_impute = 500 and
N_mcmc = 1000. Specify them explicitly when reproducing a
previous simulation design.h0. Use a method
whose effect scale supports the intended nonzero margin.Internal analysis code is grouped into analysis_*.R
files, with separate files for method implementations, shared
calculations, and analysis stages. This source reorganization preserves
function names and calculations.
method = "rmst" adds a two-arm restricted mean
survival time difference Wald analysis to simulations and observed
interim monitoring. The fixed rmst_tau defaults to
end_of_study; effects and null margins are in time units,
with longer event-free time tested using
alternative = "greater". Observed censoring is retained,
and final multiple imputation pools RMST differences and Greenwood
variances using Rubin’s rules. Unsupported positive survival tails and
zero total variance produce explicit non-estimability errors. A worked
vignette and reference, calibration, and reproducibility checks
accompany the option.
A new calibration vignette demonstrates how to screen a
prespecified grid of prob_ha values against a one-sided
type I error target, classify candidates using Monte Carlo uncertainty,
and independently validate a selected threshold with an independent
simulation seed (#67).
Interim monitoring now supports an immediate-success boundary,
Qn, on the same predictive probability used to stop accrual
for expected success. At each look, the trial declares immediate success
when that probability is above Qn; otherwise it stops
accrual and follows enrolled participants when it is above
Sn, stops for futility when the predictive probability at
the maximum sample size is below Fn, or continues. The
default Qn = 1 preserves the previous three-decision
design. A new ThermoCool AF vignette illustrates the four-decision
design without adding it to the existing worked examples (#16).
Frequentist binary analyses now require an explicit choice
between method = "riskdiff-fm", a Farrington-Manning score
test that remains defined for sparse boundary tables, and
method = "riskdiff-wald", the previous plug-in Wald test.
The former method = "riskdiff" remains as a deprecated
alias for "riskdiff-wald" and emits a warning. Both
explicit methods support nonzero risk-difference margins and all three
alternatives. Imputed final Wald risk-difference analyses use Rubin’s
scalar pooling rules and require positive total variance. FM final
imputation is unsupported.
For fixed-time binary endpoints, each completed predictive
replicate is now summarized by the event count and sample size in each
arm. These are the sufficient statistics for the risk-difference and
beta-binomial analyses. Identical sufficient statistics are evaluated
once for deterministic analyses. With bin_method = "mc",
every replicate continues to receive fresh beta-posterior draws, even
when its counts match an earlier replicate. Predictive probabilities and
stopping rules are unchanged. Interim results report how often identical
completed-data summaries occurred and were reused (#91).
Interim log-rank, Cox, and Bayesian survival predictions now prepare the observed follow-up, event indicators, treatment assignments, and imputation positions once per look. Each predictive replicate replaces only the follow-up and event values that were imputed, while fixed treatment assignments are reused. The completed-data analyses, predictive probabilities, stopping rules, and random-number sequence are unchanged. This reduces the time and memory required for repeated interim analyses without changing the statistical procedure (#39).
Additional statistical validation now covers piecewise-exponential event and conditional-imputation distributions, conjugate Gamma posterior moments, frequentist type I error and confidence-interval coverage, and invariance to the choice of time unit. Longer non-CRAN reference calculations also check predictive probabilities close to the futility and expected-success thresholds. Acceptance limits reflect Monte Carlo uncertainty and identify the estimand, target, estimate, and uncertainty when a check fails.
Function documentation now states argument types, accepted lengths and ranges, defaults, and required inputs more consistently. Titles, descriptions, analysis-method explanations, and return values have been revised to emphasize their statistical interpretation.
All N_impute completions at an interim look are now
generated together from the corresponding posterior hazard draws. The
statistical imputation model and completed-data analyses are unchanged,
but repeated preliminary work is avoided. Imputations are generated in
posterior-draw order: current treatment, current control, future
treatment, then future control. Because all imputations now precede the
completed-data analyses, a given seed can produce different Bayesian
results than earlier package versions. Repeated runs and supported
serial or parallel calculations remain reproducible (#37).
Bayesian survival calculations now use analysis-interval widths
that always extend from time zero to end_of_study. They are
not shortened when no participant has yet reached the maximum follow-up
time at an interim or final analysis. This preserves the specified
cumulative-hazard estimand while avoiding repeated calculation of the
same interval widths (#90).
Repeated Bayesian survival analyses of completed predictive datasets now work directly from arm-by-interval event counts and exposure times. These are the sufficient statistics for the conjugate Gamma posterior. The posterior distribution, treatment-effect calculation, empty-interval policy, and reported results are unchanged (#89).
Documentation now states explicitly that Bayesian binary prediction uses a piecewise-exponential Gamma model to impute pending outcomes and a separate beta-binomial model to analyze completed endpoint statuses. These are separate models rather than one joint Bayesian model. Retained analysis information identifies both priors and the imputation horizon (#64).
prior_surv and prior_surv_final now
accept independent arm-specific Gamma priors supplied as lists named
control and treatment (or
treatment for a single-arm design). Each arm may use one
shape-rate pair across intervals or an interval-specific matrix.
Existing shared priors remain supported and preserve seeded results.
Individual trial traces and externally evaluated interim looks report
prior and posterior parameters by arm and interval. Priors remain
independent; there is no hierarchical or implicit borrowing between arms
(#88).
New evaluate_interim() applies the same posterior
predictive decision rule used by survival_adapt() to an
observed interim data cut. Follow-up status distinguishes events,
completed follow-up, pending outcomes, and early censoring. The
potential number of future participants in each arm is derived from
N_total, observed enrollment, and rand_ratio;
block details and unobserved future assignments are not required.
Results include predictive probabilities, Monte Carlo uncertainty,
allocation and imputation summaries, a one-look decision trace, and the
design information needed for audit (#81).
survival_adapt() and sim_trials() now
allow event-time generation and the predictive model to use different
piecewise-exponential partitions. generation_cutpoints
defines the intervals for hazard_treatment and
hazard_control; cutpoints defines the
intervals for posterior estimation, predictive imputation, Bayesian
survival analysis, and interval-specific priors.
generation_cutpoints defaults to cutpoints,
preserving existing calls and seeded results. In direct calls to
sim_comp_data(), the previous named argument
cutpoints must now be written as
generation_cutpoints (#95).
rand_ratio and
randomization(allocation =) now accept vectors named
control and treatment in either order. Unnamed
vectors continue to mean c(control, treatment); unequal
unnamed allocations warn that names may be required in a future major
release. Retained analysis information records the control and treatment
allocation weights in that order.
prop_loss now accepts separate dropout probabilities
at end_of_study for the control and treatment arms through
a named vector. A single value continues to apply the same dropout
distribution to both arms. Dropout is sampled separately within each
randomized arm, and single-arm designs continue to require one value
(#69).
Piecewise event and enrollment times now use the survival
counting-process convention (start, stop]: a time exactly
at a cutpoint belongs to the interval ending at that cutpoint. Posterior
sufficient statistics and piecewise enrollment calculations use this
convention consistently. PWEALL continues to generate continuous event
times using [start, stop) hazard intervals; the two
conventions differ only at individual boundary points, which have
probability zero under a continuous distribution (#70).
Results from survival_adapt() and
sim_trials() now retain all evaluated design and analysis
settings, including defaults. These values can be saved and supplied to
the same function with do.call() to reproduce or modify an
analysis without reconstructing the original call (#86).
summarise_sims() now reports the numbers of
requested, successfully analyzed, failed, and included trials. Every
estimated probability and mean sample size is accompanied by a 95% Monte
Carlo interval. Wilson intervals remain informative when no trials, or
all trials, have the outcome. The optional max_mcse
criterion warns when a scenario has insufficient Monte Carlo precision
and distinguishes simulation uncertainty from clinical or model
uncertainty (#68).
New summarise_calendar_time() reports operating
characteristics for trial duration, accrual, analysis readiness,
person-time, peak concurrent follow-up, and interim-look timing.
Calendar time begins at first enrollment, and the requested, analyzed,
and failed trial counts remain explicit (#87).
imputed_final = TRUE. Genuine FM
final imputation is now rejected because no validated pooling rule is
implemented. FM simulations requesting final imputation require zero
dropout in both arms and reject unsupported configurations before
generating trials.prior_surv to
generate outstanding outcomes and prior_surv_final to
analyze each hypothetical completed trial, at both the current and
maximum sample sizes. This permits external evidence in prediction while
retaining a separate final analysis prior.
evaluate_interim() now accepts
prior_surv_final and records both priors. The default
prior_surv_final = prior_surv preserves previous
calculations. Bayesian survival designs with different priors may change
their stopping decisions and operating characteristics and should be
recalibrated.prop_loss = p
specifies P(D <= end_of_study) = p, using rate
-log1p(-p) / end_of_study. Observation ends at the first
event, dropout, or administrative horizon; events before dropout are
retained. The observed fraction censored by dropout can be lower than
p, and counts vary between trials instead of being fixed by
ceiling(). Probabilities must be in [0, 1);
1 is no longer accepted. Positive dropout assumptions
change seeded results and operating characteristics;
prop_loss = 0 preserves results and RNG use. Vignettes and
help now explain this interpretation, the migration from the previous
dependent-censoring mechanism, and the limitations of complete-case
binary analysis with dropout. Statistical checks cover dropout
probabilities, event preservation, and Kaplan-Meier recovery with common
and arm-specific dropout under constant and piecewise event
hazards.goldilocks
and its compiled log-rank calculation correctly on Windows, including
when vignettes are built from the source package.summarise_sims() now calculates the probability of
reaching the maximum sample size from the trial-level stopping
indicators. Previously this probability could be incorrect because the
futility probability and the trial-level stopping indicator used the
same name during the calculation (#68).sim_trials() now limits the number of workers to the
number useful for the requested trials and starts parallel calculation
only when there are at least two trials per worker. This avoids parallel
setup for fewer than four trials. Worker processes are closed after an
error, and timing summaries separate setup time from simulation time
(#93).sim_trials() call. Successful trials are
retained, failed trials are listed separately, and one warning gives the
number affected. If every trial fails, the calculation still stops and
provides the failure details. Summary denominators report requested,
analyzed, and failed trials explicitly (#72).survival package and otherwise use
survival::coxph(). Both calculations target the same Wald
test. Singular fits, analyses with no events, and convergence warnings
now produce specific non-estimability errors (#77).summarise_sims() now accepts a complete
sim_trials() result as well as a trial-level results table.
Named or grouped scenario identifiers are retained, together with the
seed, parallel method, failures, and design settings. Requested,
analyzed, and failed trial counts remain explicit so failures cannot
silently change the denominator (#78).N_impute = 500 and
N_mcmc = 1000, and decision traces report estimates,
diagnostic Monte Carlo standard errors and exact bounds, draw counts,
uncertainty counts, and the decision reason (#60).empty_interval = "propagate" behavior
remains available only as an explicit legacy heuristic. To reproduce
earlier results, set it explicitly; otherwise review the
interval-specific prior because it now supplies all information for an
empty interval. Decision traces identify every interval handled by
either policy (#62).h0 values instead
of silently testing the equal-survival null. Cox margins remain on the
log-hazard-ratio scale, while Bayesian and risk-difference margins
remain on their documented event- probability scales (#73).expm1() and log1p() transformations, retaining
precision near zero and defining exact behavior at zero and infinite
boundaries (#76).N_mcmc does not
correct approximation error and describe the alternative methods.prior_surv_final is used in Bayesian survival success tests
inside interim predictions, and that omitting it shares
prior_surv across both roles. Distinguish this from
Bayesian binary analysis.plot_enrollment() displays the expected enrollment
curve, simulated trial trajectories, interim looks, and the maximum
sample size for constant or piecewise enrollment rates. Design settings
may be supplied directly or taken from a survival_adapt()
or sim_trials() result.prior_surv
and prior_surv_final, and the Bayesian binary prior through
prior_bin. prior_surv_final defaults to
prior_surv but may specify a different Gamma prior for
final imputation and analysis. A survival prior may be one shape-rate
pair shared across intervals or an interval-specific matrix. The ADVENT
vignette illustrates interval- and stage-specific priors (#59).enrollment() now simulates exact continuous arrival times
from a piecewise-constant Poisson process. This handles fractional
changes in enrollment rate without dividing follow-up into unit-time
bins. lambda_time = NULL represents a constant rate, and
every positive change time requires one additional value of
lambda. Time zero is the first-patient-in origin (#55).
Existing calls must replace lambda_time = 0 with
NULL and remove the leading zero from piecewise schedules.
Results obtained with a fixed seed will differ from earlier versions,
and estimated operating characteristics may change, particularly for low
rates or fractional change times.method = "chisq" analysis has
been replaced by method = "riskdiff". It estimates the
treatment-minus-control event-risk difference and supports one- or
two-sided Wald tests against h0. With
imputed_final = TRUE, estimates and within-imputation
variances are combined using Rubin’s rules. The previous
mean-of-1 - P rule is no longer used. Imputed log-rank
final analyses remain unsupported (#56).imputed_final = TRUE. Log
hazard ratios and within-imputation variances are combined using Rubin’s
rules, and post_prob_ha reports 1 - P from the
pooled Wald test. Analyses without final imputation are unchanged
(#56).N_mcmc for the number of posterior draws; the separate
bin_N argument has been removed. Results retain the
posterior mean treatment effect rather than all individual posterior
draws.survival_adapt() can now retain an interim decision
trace containing predictive probabilities, decision thresholds, stopping
outcomes, arm-level sample sizes, and relevant warnings. New functions
summarize and plot traces from individual trials and simulation studies
(#57).plot_sim_stopping() now provides marginal, conditional,
cumulative, and flowchart summaries of stopping outcomes by enrolled
sample size. Plot denominators are stated explicitly, and reached looks
with no stopping events are retained when simulation traces are
available.plot_sim_ocs() displays success probability,
stopping probability, and expected sample size across treatment-effect
scenarios.sim_trials(return_trace = TRUE) retains interim results
across simulated trials, and new plot_sim_decisions()
displays predictive probabilities and decision regions at each interim
look.sim_trials() now supports reproducible parallel
simulation on Windows, macOS, and Linux. The backend
argument selects the parallel method, with backend = "auto"
choosing an appropriate method for the operating system. Setting
seed leaves the caller’s random-number state unchanged
(#40).method = "bayes-surv", distinguishing it from the new
beta-binomial method = "bayes-bin" for complete binary
outcomes.binary_imputation = "bernoulli" to draw event status from
the conditional piecewise-exponential event probability. Conditional
event-time imputation remains the default (#21).h0 is now the null log hazard ratio.
Non-inferiority may therefore be specified as
h0 = log(margin).survival_adapt()
simulations (#42).treatment = 1 for the treatment arm and
treatment = 0 for the control arm (#35).seed argument to sim_trials() creates
independent random-number streams for individual trials, giving
reproducible results with one or several cores (#41).N_mcmc = 1.sim_trials() now uses the same default alternative
hypothesis and futility threshold as survival_adapt(), so
omitted arguments define the same design (#47).maxtime is required when the final hazard is zero
(#49).h0 must be one finite value and must satisfy the
relevant probability bounds for Bayesian analyses (#50).cutpoints now contains only the interior hazard-change
times. NULL represents a constant hazard, and every
cutpoint requires one additional hazard rate (#58). Cutpoints must be
finite, positive, and strictly increasing, with the study endpoint after
the final cutpoint (#52).enrollment() now checks the full schedule, including
the sample size, positive enrollment rates, and strictly increasing
change times, before generating data (#53).sim_trials() now defaults to ncores = 1L,
avoiding unexpected parallel calculation when the argument is omitted
(#54).sim_trials() uses one core when the available number of
cores cannot be determined.c(0, 1) are
rejected.prop_to_haz() now rejects cumulative event
probabilities that are non-finite, outside [0, 1), or
decreasing over time.pwe_impute() now rejects a maxtime earlier
than the participant’s observed follow-up time.Fn = 0, futility monitoring remains disabled
throughout the trial (#33).empty_interval now determines whether an interval with
no observed exposure borrows information from an adjacent interval, uses
its prior alone, or produces an error.plot_enrollment(), plot_sim_ocs(),
plot_sim_stopping(), and
plot_sim_decisions().method = "bayes-bin" for two-arm and single-arm complete
binary endpoint analyses.method = "bayes-bin" with beta-binomial non-inferiority
endpoints, adaptive sample-size thresholds, and pre-computed simulation
examples.goldilocks treats enrollment time and
randomization time as the same time point in its time-to-event
simulations.h0 is
often referred to as a performance goal (PG) or objective performance
criterion (OPC).Fn documentation to state that
Fn = 0 disables futility monitoring.alternative = "greater" or "less". The
chi-square analysis remains two-sided (#20).posterior() now warns when a piecewise interval
contains no participants and information is carried forward from an
adjacent interval.fastlogranktest package that were no longer used
and adopted the base R pipe in package calculations.interim_look = NULL now proceed directly
to the final analysis.impute_data() now continues to work when the supplied
dataset contains additional columns (#26).est_interim element from the
survival_adapt() return-value documentation. This field was
documented but never computed or returned.method = "bayes-surv" with imputation, clarifying that the
imputation model’s posterior influences the analysis posterior
(#27).prop_loss parameter documentation, explaining
that LTFU times are drawn from Uniform(0, t) and that the
event has not yet occurred at the dropout time (#25).interim_look requirement (at
least the block size for two-arm designs) in the
survival_adapt() interim_look parameter.summarise_sims() operating characteristics are now rendered
as captioned tables, a section documents one-sided tests (including that
method = "bayes-surv" requires a one-sided alternative and
measures the effect on the cumulative-failure-probability scale
p_treatment - p_control against h0), and the
cutpoint argument name was corrected to
cutpoints.method = "bayes-surv" with a
piecewise hazard via cutpoints and
prop_to_haz(), the Gamma-prior / posterior decision rule on
the cumulative-failure-probability scale, and a worked single-trial
example.hazard_control = NULL mode (Bayesian-only), the role of
h0 as a benchmark failure rate, the success rule
Pr(p_treatment < h0) > prob_ha, and a worked
single-trial example with operating-characteristics templates.fastlogranktest package.prior uses a Gamma shape-rate
parameterization and is shared across piecewise intervals and treatment
arms.fastlogranktest is now included in goldilocks,
because fastlogranktest is no longer available on
CRAN.summarise_sims().sim_comp_data(),
test_stop_success(), and test_final(),
respectively.NEWS.md file to track changes to the
package.These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.