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risk.times output@info slot as
info$followup.unique and
info$followup.nonunique (per subgroup, mirroring
info$outcome.unique / info$outcome.nonunique).
Both are grouped by baseline treatment over expanded-data rows with an
observed (non-NA) outcome - the person-time the outcome
model is fit on. The non-unique table counts follow-up intervals (so the
non-unique outcome counts divided by these give per-arm event rates);
the unique table counts the distinct subjects contributing follow-up to
each arm. Also shown in the printed diagnostic tables.coxph(formula, data), avoiding the
model.frame/model.matrix rebuild on every
bootstrap iteration: survival::coxph.fit() for the
non-competing-event model and survival::agreg.fit() for the
competing-event Fine-Gray (counting-process) model. The hazard ratio and
CIs are unchanged.finegray() case weights (fgwt), which are
required for a valid subdistribution-hazard estimate and were previously
omitted. This is a no-op for the current hazard simulation (which has
only administrative censoring, so all fgwt are 1) but
corrects the estimate should the simulated data ever carry random
censoring.@info
slot as info$compevent.unique and
info$compevent.nonunique, mirroring the structure of
info$outcome.unique / info$outcome.nonunique.
Both are grouped by baseline treatment; the non-unique table counts all
competing event occurrences in the expanded data and the unique table
counts distinct subjects who experienced the competing event. Both are
NA when no compevent is specified.SEQuential() fit, populate
weight.statistics and outcome.model when
hazard = TRUE.numerator and denominator
weight models are given identical covariates. In that case the
stabilized weights all equal 1 (i.e., no weighting), which is usually a
typo in the denominator argument.SEQuential() helpfile by adding a
per-protocol example.selection.random = TRUE
retains all treated trial-starts, subsamples control trial-starts to the
requested selection.prob fraction, and is reproducible
under a fixed seed; rename the previous smoke test that did not actually
exercise the feature.weight.lower/weight.upper truncation,
weight.p99 truncation,
followup.include/trial.include,
followup.class, weight.lag_condition,
followup.min/followup.max, and
weight.eligible_cols.numerator() and denominator()
returning NULL for every weighted model; they now return
the fitted per-arm numerator/denominator weight models.inline.pred() in the weight
modelsSEQOpts() argument ordering.params@data divergence so expansion uses the
checked and repaired datainternal.hazard()internal.survival() so
each replicate standardizes over the same resample its outcome model was
fit onout), by hoisting row-index computations out of
DT[i] expressions where columns shadow local variablesfastglm.method validation to accept fastglm’s full
range 0-5 (0, column-pivoted QR, is fastglm’s own default and was
previously rejected; 4 is the full-pivoted QR and 5 the Bidiagonal
Divide and Conquer SVD; all six have been supported since fastglm 0.0.1)
and update the documentation accordinglyseed so unseeded runs are genuinely
random. Previously the full .Random.seed vector was stored
and set.seed() silently used only its first element - the
RNG kind code, a constant - so every unseeded run drew identical
bootstrap resamples. The default is now a single random integer drawn
when SEQopts() is called.followup.max is set while survival.max is left
at its Inf default. The check now runs after the
Inf defaults are resolved, which also catches the
previously missed case of a finite survival.max exceeding
the data-derived followup.max.show() erroring on a SEQoutput object
when both subgroup and compevent are specified
(the competing event section passed the model list rather than the
subgroup name to cat()); empty competing event model
entries are now also skipped instead of printing NULL.orig_id * multiplier + copy index) exceeds the 2^53
exact-integer range of doubles for roughly 8+ digit IDs, where
consecutive copy indices round to the same value and distinct copies of
a subject collapse together under by-ID grouping (corrupting
cumulative-product weights). Relabeling now falls back to string
concatenation whenever the arithmetic could overflow, or when IDs are
negative or non-integer."_sq" suffix in the pre-expansion
weight data so a custom indicator.squared no longer fails
with “column not found” in the weight models.method = "dose-response" erroring with a custom
indicator.squared. The expansion created
dose_sq/trial_sq with hardcoded names (and
excluded only the literal dose_sq from expansion variables)
while the default covariates referenced
paste0("dose", indicator.squared), so any non-default
indicator failed in SEQexpand(). All internally generated
squared columns (dose, trial, on both the
expanded data and the survival prediction grid) now follow
indicator.squared, matching the existing convention for
followup, trial and time.trialID construction from the
survival-curve standardization. The per-row paste0() label
was built on the full standardization population (and again on every
bootstrap resample) but never read: predictions there are row-wise with
no by-ID grouping, so bootstrap multiplicity is carried by the
duplicated rows themselves. Results are unchanged.pmax()/pmin() instead of evaluating scalar
max()/min() once per row via
by = .I. Results are unchanged.internal.weights() to
the columns the weight models actually use (ids, structure, treatment
and its baseline copy, formula covariates, censoring/visit/eligibility
indicators, excused flags) instead of copying every column of the
expanded (post-expansion weighting) or input (pre-expansion weighting)
table on every bootstrap iteration. Results are unchanged.SEQexpand() with nomatch = NULL (and remove a
no-op .SDcols there). Original-data rows with no
expansion-grid match - possible under followup.min > 0
or selection.random - were carried as NA-trial rows through
the squared-column computation only to be discarded by the subsequent
inner join with the baseline table. Results are unchanged.seq.int(1:.N) with seq_len(.N) in
the hazard simulation’s per-trial follow-up construction, avoiding a
double allocation per (id, trial) group; the column is now integer,
matching the expansion’s convention. Results are unchanged.glm.package = "parglm" and
bootstrap = TRUE only the main fit uses parglm: the
bootstrap refits always use fastglm, warm-started from the main fit’s
coefficients, which is faster per resample than parglm’s per-fit thread
setup. This was previously a silent switch.SEQestimate() since it is only accurate
to an order of magnitude.risk.comparison
output: RD SE (standard error of the risk difference,
natural scale) and log(RR) SE (standard error of the log
risk ratio). Both are reported whenever bootstrap = TRUE,
regardless of bootstrap.CI_method (the SEs were already
computed internally to form the "se" confidence intervals
but were not retained, and not computed at all under
"percentile").risk.comparison and risk.data, which were
hardcoded as 95% regardless of bootstrap.CI.
They now reflect the requested level (e.g. RD 90% LCI,
90% UCI when bootstrap.CI = 0.9).followup = k correctly represents survival after
k intervals, adding a row at
followup = survival.max + 1 for the final interval’s
estimate.outcome=0 rows from
subsequent periods by truncating each trial at the first event row
(thanks, @francescazaccagnino)expand.only option to SEQopts(). When
TRUE, SEQuential() returns the expanded
data.table directly and skips the analysis steps, for users
who want to inspect or store the expanded dataset on its own.followup.spline = TRUE so the basis is genuinely
non-linear. Splines are now built into the model formula via
splines::ns() instead of being applied as a single-column
transform of followup, and the new
followup.spline.df option (default 4) controls
the number of basis functions. The treatment-by-followup interaction now
uses the same spline basis. Knots are baked from the full expanded
followup once at fit time so the basis is identical at fit
and prediction time across bootstraps and survival grids. Internally,
formula column extraction now uses all.vars(), so
user-supplied covariates may include ns(),
bs(), I(), factor(),
poly() etc. without breaking expansion.format.time() to format_time()
because it wasn’t an S3 method and hence was causing roxygen2 to write
incorrect information in its helpfile.selection.random not being propagated from
SEQopts() to internal parameters.data.table to 2 threads during tests and vignette
builds, and skip the multisession parallel test on CRAN, to comply with
CRAN’s 2-core policy for checks.SEQopts(nthreads = ...) setting to
data.table during SEQuential(). Previously it
was only used by the parglm backend and ignored in the
default serial fastglm path, so data.table ran
at its global default thread count. The previous global setting is
restored when the call finishes.risk.times option to SEQopts(). When
km.curves = TRUE, risk difference and risk ratio (with CIs)
are reported at each requested follow-up time, not just at the end of
follow-up. Requested times are snapped to the latest available follow-up
at or before them, and the final time is always included. The
risk.comparison and risk.data tables gain a
Followup column.factorize() to also coerce categorical (character)
time-varying covariates - and their baseline (_bas)
counterparts - to factors with levels fixed from the full data.
Previously only fixed and treatment columns were factorized, so a
character time-varying covariate could realise different level sets
across bootstrap resamples and raise “newdata provided does not match
fitted model” (most often in bootstrapped hazard analyses on larger
samples or with a smaller bootstrap.sample). Numeric
time-varying covariates are left unchanged.@outcome.modelkm_curve()local() wrappers and made several code
optimizations.internal.weights().SEQopts().SEQuential() time.col validation
detecting and repairing non-zero-indexed time.eligible.col valuestime_varying.cols and
fixed.colsSEQopts()treat.level values exist in the
treatment columnexcused.cols flagsfollowup.min/max
orderingtreat.level length validation for multinomial and
non-multinomial analysescense.eligible and
weight.eligible_colsfollowup.min and
weight.lower from -Inf to 0internal.plot()km_curve() returning list instead of ggplot for
non-subgroup casekm_curve() subtitle conditionrisk.comparison() CIs being NA with
competing eventscbind() with := in expansion chain
to avoid intermediate copymerge() with data.table native join in
expansion data_list combine stepFALSEmatch(TRUE, ...) instead of
which(...)[1] to find first switch/event per groupsapply loop with single matrix multiply in
multinomial predictioncopy() in data_all construction and free data
list in internal_survival.R to reduce peak memory during bootstrapfollowup==0 before adding trialID in
internal.survival to avoid copying entire expanded datasetset.seed() call in
internal.hazard() to make main estimate reproducible. And
also implement fix to ensure the bootstrapping, including both standard
error and percentiles, is deterministic given the seed.hazard_ratio() function now correctly describes the
estimate as “Hazard ratio”covariates() function now returns more nicely
formatted output (with spaces around ~ and +
symbols in the model formulae)table() call with data.table’s
.Ngc() callscopy()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.